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Color Texture Description Based on Holistic and Hierarchical Order-Encoding Patterns

Tiecheng Song, Jie Feng, Yuanlin Wang, Chenqiang Gao

Auto-TLDR; Holistic and Hierarchical Order-Encoding Patterns for Color Texture Classification

Abstract Slides Poster

Local binary pattern (LBP), as one of the most representative texture operators, has attracted much attention in computer vision applications. Many LBP variants were developed in the literature. However, most of them were designed for gray images and their performance remains to be improved for color images. In this paper, we propose a novel color image descriptor named Holistic and Hierarchical Order-Encoding Patterns (H2OEP) for texture classification. In H2OEP, the holistic order-encoding pattern compactly encodes color order variation tendencies for each pixel in color space. The hierarchical order-encoding pattern leverages min ordering, median ordering and max ordering to encode local neighboring relationships across different color channels. Finally, the generated order-encoding patterns are aggregated via central pixel encoding to build 3D joint histograms for image representation. Experiments on four benchmark texture databases demonstrate the effectiveness of the proposed descriptor for color texture classification.

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First and Second-Order Sorted Local Binary Pattern Features for Grayscale-Inversion and Rotation Invariant Texture Classification

Tiecheng Song, Yuanjing Han, Jie Feng, Yuanlin Wang, Chenqiang Gao

Auto-TLDR; First- and Secondorder Sorted Local Binary Pattern for texture classification under inverse grayscale changes and image rotation

Abstract Slides Poster Similar

Local binary pattern (LBP) is sensitive to inverse grayscale changes. Several methods address this problem by mapping each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited discriminative power. In this paper, we introduce a histogram sorting method to preserve the distribution information of LBP codes and their complements. Based on this method, we propose first- and secondorder sorted LBP (SLBP) features which are robust to inverse grayscale changes and image rotation. The proposed method focuses on encoding difference-sign information and it can be generalized to embed other difference-magnitude features to obtain complementary representations. Experiments demonstrate the effectiveness of our method for texture classification under(linear or nonlinear) grayscale-inversion and rotation changes.

Local Grouped Invariant Order Pattern for Grayscale-Inversion and Rotation Invariant Texture Classification

Yankai Huang, Tiecheng Song, Shuang Li, Yuanjing Han

Auto-TLDR; Local grouped invariant order pattern for grayscale-inversion and rotation invariant texture classification

Abstract Slides Poster Similar

Local binary pattern (LBP) based descriptors have shown effectiveness for texture classification. However, most of them encode the intensity relationships between neighboring pixels and a central pixel into binary forms, thereby failing to capture the complete ordering information among neighbors. Several methods have explored intensity order information for feature description, but they do not address the grayscale-inversion problem. In this paper, we propose an image descriptor called local grouped invariant order pattern (LGIOP) for grayscale-inversion and rotation invariant texture classification. Our LGIOP is a histogram representation which jointly encodes neighboring order information and central pixels. In particular, two new order encoding methods, i.e., intensity order encoding and distance order encoding, are proposed to describe the neighboring relationships. These two order encoding methods are not only complementary but also invariant to grayscale-inversion and rotation changes. Experiments for texture classification demonstrate that the proposed LGIOP descriptor is robust to (linear or nonlinear) grayscale inversion and image rotation.

Local Binary Quaternion Rotation Pattern for Colour Texture Retrieval

Hela Jebali, Noel Richard, Mohamed Naouai

Auto-TLDR; Local Binary Quaternion Rotation Pattern for Color Texture Classification

Abstract Poster Similar

Color is very important feature for image representation, it assumes very essential in the human visual recognition process. Most existing approaches usually extract features from the three color channels separately (Marginal way). Although, it exists few vector expressions of texture features. Aware of the high interaction that exists between different channels in the color image, this work introduces a compact texture descriptor, named Local Binary Quaternion Rotation Pattern (LBQRP). In this LBQRP purpose, the quaternion representation is used to represent color texture. The distance between two color can be expressed as the angle of rotation between two unit quaternions using the geodesic distance. After a LBQRP coding, local histograms are extracted and used as features. Experiments on three challenging color datasets: Vistex, Outex-TC13 and USPtex are carried out to evaluate the LBQRP performance in texture classification. Results show the high efficiency of the proposed approach facing to several stat-of-art methods.

Gaussian Convolution Angles: Invariant Vein and Texture Descriptors for Butterfly Species Identification

Xin Chen, Bin Wang, Yongsheng Gao

Auto-TLDR; Gaussian convolution angle for butterfly species classification

Abstract Slides Poster Similar

Identifying butterfly species by image patterns is a challenging task in computer vision and pattern recognition community due to many butterfly species having similar shape patterns with complex interior structures and considerable pose variation. In additional, geometrical transformation and illumination variation also make this task more difficult. In this paper, a novel image descriptor, named Gaussian convolution angle (GCA) is proposed for butterfly species classification. The proposed GCA projects the butterfly vein image function and intensity image function along a group of vectors that start from a common contour points and ends at the remaining contour points which results a group of vectors that capture the complex vein patterns and texture patterns of butterfly images. The Gaussian convolution of different scales is conducted to the resulting vector functions to generate a multiscale GCA descriptors. The proposed GCA is not only invariant to geometrical transformation including rotation, scaling and translation, but also invariant to lighting change. The proposed method has been tested on a publicly available butterfly image dataset that has 832 samples of 10 species. It achieves a classification accuracy of 92.03% which is higher than the benchmark methods.

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou, Ke Shu, Peng Liu, Jianwen Xiang, Shengwu Xiong

Auto-TLDR; LDN-TOP Representation followed by ProCRC Classification for Face Anti-Spoofing

Abstract Slides Poster Similar

Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel "LDN-TOP representation followed by ProCRC classification" pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN-TOP). The multi-scale LDN-TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

Appliance Identification Using a Histogram Post-Processing of 2D Local Binary Patterns for Smart Grid Applications

Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira

Auto-TLDR; LBP-BEVM based Local Binary Patterns for Appliances Identification in the Smart Grid

Abstract Similar

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks.

A Distinct Discriminant Canonical Correlation Analysis Network Based Deep Information Quality Representation for Image Classification

Lei Gao, Zheng Guo, Ling Guan Ling Guan

Auto-TLDR; DDCCANet: Deep Information Quality Representation for Image Classification

Abstract Slides Poster Similar

In this paper, we present a distinct discriminant canonical correlation analysis network (DDCCANet) based deep information quality representation with application to image classification. Specifically, to explore the sufficient discriminant information between different data sets, the within-class and between-class correlation matrices are employed and optimized jointly. Moreover, different from the existing canonical correlation analysis network (CCANet) and related algorithms, an information theoretic descriptor, information quality (IQ), is adopted to generate the deep-level feature representation for image classification. Benefiting from the explored discriminant information and IQ descriptor, it is potential to gain a more effective deep-level representation from multi-view data sets, leading to improved performance in classification tasks. To demonstrate the effectiveness of the proposed DDCCANet, we conduct experiments on the Olivetti Research Lab (ORL) face database, ETH80 database and CIFAR10 database. Experimental results show the superiority of the proposed solution on image classification.

Label Self-Adaption Hashing for Image Retrieval

Jianglin Lu, Zhihui Lai, Hailing Wang, Jie Zhou

Auto-TLDR; Label Self-Adaption Hashing for Large-Scale Image Retrieval

Abstract Slides Poster Similar

Hashing has attracted widespread attention in image retrieval because of its fast retrieval speed and low storage cost. Compared with supervised methods, unsupervised hashing methods are more reasonable and suitable for large-scale image retrieval since it is always difficult and expensive to collect true labels of the massive data. Without label information, however, unsupervised hashing methods can not guarantee the quality of learned binary codes. To resolve this dilemma, this paper proposes a novel unsupervised hashing method called Label Self-Adaption Hashing (LSAH), which contains effective hashing function learning part and self-adaption label generation part. In the first part, we utilize anchor graph to keep the local structure of the data and introduce joint sparsity into the model to extract effective features for high-quality binary code learning. In the second part, a self-adaptive cluster label matrix is learned from the data under the assumption that the nearest neighbor points should have a large probability to be in the same cluster. Therefore, the proposed LSAH can make full use of the potential discriminative information of the data to guide the learning of binary code. It is worth noting that LSAH can learn effective binary codes, hashing function and cluster labels simultaneously in a unified optimization framework. To solve the resulting optimization problem, an Augmented Lagrange Multiplier based iterative algorithm is elaborately designed. Extensive experiments on three large-scale data sets indicate the promising performance of the proposed LSAH.

A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

Liping Zhang, Weijun Li, Ning Xin

Auto-TLDR; Finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns

Abstract Slides Poster Similar

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

Creating Classifier Ensembles through Meta-Heuristic Algorithms for Aerial Scene Classification

Álvaro Roberto Ferreira Jr., Gustavo Gustavo Henrique De Rosa, Joao Paulo Papa, Gustavo Carneiro, Fabio Augusto Faria

Auto-TLDR; Univariate Marginal Distribution Algorithm for Aerial Scene Classification Using Meta-Heuristic Optimization

Abstract Slides Poster Similar

Aerial scene classification is a challenging task to be solved in the remote sensing area, whereas deep learning approaches, such as Convolutional Neural Networks (CNN), are being widely employed to overcome such a problem. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the nurturing of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized-ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Finally, one can observe that the Univariate Marginal Distribution Algorithm (UMDA) overcame popular literature meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization considering the adopted criteria in the performed experiments.

Joint Learning Multiple Curvature Descriptor for 3D Palmprint Recognition

Lunke Fei, Bob Zhang, Jie Wen, Chunwei Tian, Peng Liu, Shuping Zhao

Auto-TLDR; Joint Feature Learning for 3D palmprint recognition using curvature data vectors

Abstract Slides Poster Similar

3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. Then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images

Mohammad Alkhatib, Adel Hafiane, Pierre Vieyres

Auto-TLDR; A Deep Neural Network for Deep Neural Networks to Detect Median Nerve in Ultrasound-Guided Regional Anesthesia

Abstract Slides Poster Similar

Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.

Magnifying Spontaneous Facial Micro Expressions for Improved Recognition

Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurence Taggart, Pradeepa Samarasinghe

Auto-TLDR; Eulerian Video Magnification for Micro Expression Recognition

Abstract Slides Poster Similar

Building an effective automatic micro expression recognition (MER) system is becoming increasingly desirable in computer vision applications. However, it is also very challenging given the fine-grained nature of the expressions to be recognized. Hence, we investigate if amplifying micro facial muscle movements as a pre-processing phase, by employing Eulerian Video Magnification (EVM), can boost performance of Local Phase Quantization with Three Orthogonal Planes (LPQ-TOP) to achieve improved facial MER across various datasets. In addition, we examine the rate of increase for recognition to determine if it is uniform across datasets using EVM. Ultimately, we classify the extracted features using Support Vector Machines (SVM). We evaluate and compare the performance with various methods on seven different datasets namely CASME, CAS(ME)2, CASME2, SMIC-HS, SMIC-VIS, SMIC-NIR and SAMM. The results obtained demonstrate that EVM can enhance LPQ-TOP to achieve improved recognition accuracy on the majority of the datasets.

A Multi-Focus Image Fusion Method Based on Fractal Dimension and Guided Filtering

Nikoo Dehghani, Ehsanollah Kabir

Auto-TLDR; Fractal Dimension-based Multi-focus Image Fusion with Guide Filtering

Abstract Slides Poster Similar

Fractal Dimension (FD) is widely used for image segmentation because of its successful approach toward quantifying texture information. In this paper, we present a FD-based multi-focus image fusion method that utilizes FD to identify focused regions, as the primary step for the multi-focus image fusion process. The algorithm aims to extract the local FD features of each multi-focus pair estimated using the differential box-counting method. A guided filter is employed to further specify the spatial information and increase the robustness of the FD features to noise. The outcome would be analyzed to achieve a focus map that identifies sharp regions in each partially focused image. Afterwards, the detected regions are combined into a single all-focused image. The experiments, along with the objective assessments, demonstrate the competitive performance of the proposed method compared to several state-of-the-art multi-focus image fusion methods.

Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing

Qianqian Zhang, Yazhou Liu, Quansen Sun

Auto-TLDR; Optimized Projection Supervised Discrete Hashing for Large-Scale Remote Sensing Image Object Classification

Abstract Slides Poster Similar

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has been widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

Abstract Slides Poster Similar

In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

Automatic Tuberculosis Detection Using Chest X-Ray Analysis with Position Enhanced Structural Information

Hermann Jepdjio Nkouanga, Szilard Vajda

Auto-TLDR; Automatic Chest X-ray Screening for Tuberculosis in Rural Population using Localized Region on Interest

Abstract Slides Poster Similar

For Tuberculosis (TB) detection beside the more expensive diagnosis solutions such as culture or sputum smear analysis one could consider the automatic analysis of the chest X-ray (CXR). This could mimic the lung region reading by the radiologist and it could provide a cheap solution to analyze and diagnose pulmonary abnormalities such as TB which often co- occurs with HIV. This software based pulmonary screening can be a reliable and affordable solution for rural population in different parts of the world such as India, Africa, etc. Our fully automatic system is processing the incoming CXR image by applying image processing techniques to detect the region on interest (ROI) followed by a computationally cheap feature extraction involving edge detection using Laplacian of Gaussian which we enrich by counting the local distribution of the intensities. The choice to ”zoom in” the ROI and look for abnormalities locally is motivated by the fact that some pulmonary abnormalities are localized in specific regions of the lungs. Later on the classifiers can decide about the normal or abnormal nature of each lung X-ray. Our goal is to find a simple feature, instead of a combination of several ones, -proposed and promoted in recent years’ literature, which can properly describe the different pathological alterations in the lungs. Our experiments report results on two publicly available data collections1, namely the Shenzhen and the Montgomery collection. For performance evaluation, measures such as area under the curve (AUC), and accuracy (ACC) were considered, achieving AUC = 0.81 (ACC = 83.33%) and AUC = 0.96 (ACC = 96.35%) for the Montgomery and Schenzen collections, respectively. Several comparisons are also provided to other state- of-the-art systems reported recently in the field.

DSPNet: Deep Learning-Enabled Blind Reduction of Speckle Noise

Yuxu Lu, Meifang Yang, Liu Wen

Auto-TLDR; Deep Blind DeSPeckling Network for Imaging Applications

Abstract Poster Similar

Blind reduction of speckle noise has become a long-standing unsolved problem in several imaging applications, such as medical ultrasound imaging, synthetic aperture radar (SAR) imaging, and underwater sonar imaging, etc. The unwanted noise could lead to negative effects on the reliable detection and recognition of objects of interest. From a statistical point of view, speckle noise could be assumed to be multiplicative, significantly different from the common additive Gaussian noise. The purpose of this study is to blindly reduce the speckle noise under non-ideal imaging conditions. The multiplicative relationship between latent sharp image and random noise will be first converted into an additive version through a logarithmic transformation. To promote imaging performance, we introduced the feature pyramid network (FPN) and atrous spatial pyramid pooling (ASPP), contributing to a more powerful deep blind DeSPeckling Network (named as DSPNet). In particular, DSPNet is mainly composed of two subnetworks, i.e., Log-NENet (i.e., noise estimation network in logarithmic domain) and Log-DNNet (i.e., denoising network in logarithmic domain). Log-NENet and Log-DNNet are, respectively, proposed to estimate noise level map and reduce random noise in logarithmic domain. The multi-scale mixed loss function is further proposed to improve the robust generalization of DSPNet. The proposed deep blind despeckling network is capable of reducing random noise and preserving salient image details. Both synthetic and realistic experiments have demonstrated the superior performance of our DSPNet in terms of quantitative evaluations and visual image qualities.

Face Anti-Spoofing Using Spatial Pyramid Pooling

Lei Shi, Zhuo Zhou, Zhenhua Guo

Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing

Abstract Slides Poster Similar

Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prune to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.

Quality-Based Representation for Unconstrained Face Recognition

Nelson Méndez-Llanes, Katy Castillo-Rosado, Heydi Mendez-Vazquez, Massimo Tistarelli

Auto-TLDR; activation map for face recognition in unconstrained environments

Abstract Slides Similar

Significant advances have been achieved in face recognition in the last decade thanks to the development of deep learning methods. However, recognizing faces captured in uncontrolled environments is still a challenging problem for the scientific community. In these scenarios, the performance of most of existing deep learning based methods abruptly falls, due to the bad quality of the face images. In this work, we propose to use an activation map to represent the quality information in a face image. Different face regions are analyzed to determine their quality and then only those regions with good quality are used to perform the recognition using a given deep face model. For experimental evaluation, in order to simulate unconstrained environments, three challenging databases, with different variations in appearance, were selected: the Labeled Faces in the Wild Database, the Celebrities in Frontal-Profile in the Wild Database, and the AR Database. Three deep face models were used to evaluate the proposal on these databases and in all cases, the use of the proposed activation map allows the improvement of the recognition rates obtained by the original models in a range from 0.3 up to 31%. The obtained results experimentally demonstrated that the proposal is able to select those face areas with higher discriminative power and enough identifying information, while ignores the ones with spurious information.

Photometric Stereo with Twin-Fisheye Cameras

Jordan Caracotte, Fabio Morbidi, El Mustapha Mouaddib

Auto-TLDR; Photometric stereo problem for low-cost 360-degree cameras

Abstract Slides Poster Similar

In this paper, we introduce and solve, for the first time, the photometric stereo problem for low-cost 360-degree cameras. In particular, we present a spherical image irradiance equation which is adapted to twin-fisheye cameras, and an original algorithm for the estimation of light directions based on the specular highlights observed on mirror balls. Extensive experiments with synthetic and real-world images captured by a Ricoh Theta V camera, demonstrate the effectiveness and robustness of the proposed 3D reconstruction pipeline. To foster reproducible research, the image dataset and code developed for this paper are made publicly available at the address: https://home.mis.u-picardie.fr/~fabio/PhotoSphere.html

Embedding Shared Low-Rank and Feature Correlation for Multi-View Data Analysis

Zhan Wang, Lizhi Wang, Hua Huang

Auto-TLDR; embedding shared low-rank and feature correlation for multi-view data analysis

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The diversity of multimedia data in the real-world usually forms multi-view features. How to explore the structure information and correlations among multi-view features is still an open problem. In this paper, we propose a novel multi-view subspace learning method, named embedding shared low-rank and feature correlation (ESLRFC), for multi-view data analysis. First, in the embedding subspace, we propose a robust low-rank model on each feature set and enforce a shared low-rank constraint to characterize the common structure information of multiple feature data. Second, we develop an enhanced correlation analysis in the embedding subspace for simultaneously removing the redundancy of each feature set and exploring the correlations of multiple feature data. Finally, we incorporate the low-rank model and the correlation analysis into a unified framework. The shared low-rank constraint not only depicts the data distribution consistency among multiple feature data, but also assists robust subspace learning. Experimental results on recognition tasks demonstrate the superior performance and noise robustness of the proposed method.

Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval

Kuan-Hsun Wang, Chia Chun Cheng, Yi-Ling Chen, Yale Song, Shang-Hong Lai

Auto-TLDR; Attention-based Deep Metric Learning for Near-duplicate Video Retrieval

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Near-duplicate video retrieval (NDVR) is an important and challenging problem due to the increasing amount of videos uploaded to the Internet. In this paper, we propose an attention-based deep metric learning method for NDVR. Our method is based on well-established principles: We leverage two-stream networks to combine RGB and optical flow features, and incorporate an attention module to effectively deal with distractor frames commonly observed in near duplicate videos. We further aggregate the features corresponding to multiple video segments to enhance the discriminative power. The whole system is trained using a deep metric learning objective with a Siamese architecture. Our experiments show that the attention module helps eliminate redundant and noisy frames, while focusing on visually relevant frames for solving NVDR. We evaluate our approach on recent large-scale NDVR datasets, CC_WEB_VIDEO, VCDB, FIVR and SVD. To demonstrate the generalization ability of our approach, we report results in both within- and cross-dataset settings, and show that the proposed method significantly outperforms state-of-the-art approaches.

Depth Videos for the Classification of Micro-Expressions

Ankith Jain Rakesh Kumar, Bir Bhanu, Christopher Casey, Sierra Cheung, Aaron Seitz

Auto-TLDR; RGB-D Dataset for the Classification of Facial Micro-expressions

Abstract Slides Poster Similar

Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future.

Feature Extraction by Joint Robust Discriminant Analysis and Inter-Class Sparsity

Fadi Dornaika, Ahmad Khoder

Auto-TLDR; Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS)

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Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features. The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the $\ell_{2,1}$ constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

DFH-GAN: A Deep Face Hashing with Generative Adversarial Network

Bo Xiao, Lanxiang Zhou, Yifei Wang, Qiangfang Xu

Auto-TLDR; Deep Face Hashing with GAN for Face Image Retrieval

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Face Image retrieval is one of the key research directions in computer vision field. Thanks to the rapid development of deep neural network in recent years, deep hashing has achieved good performance in the field of image retrieval. But for large-scale face image retrieval, the performance needs to be further improved. In this paper, we propose Deep Face Hashing with GAN (DFH-GAN), a novel deep hashing method for face image retrieval, which mainly consists of three components: a generator network for generating synthesized images, a discriminator network with a shared CNN to learn multi-domain face feature, and a hash encoding network to generate compact binary hash codes. The generator network is used to perform data augmentation so that the model could learn from both real images and diverse synthesized images. We adopt a two-stage training strategy. In the first stage, the GAN is trained to generate fake images, while in the second stage, to make the network convergence faster. The model inherits the trained shared CNN of discriminator to train the DFH model by using many different supervised loss functions not only in the last layer but also in the middle layer of the network. Extensive experiments on two widely used datasets demonstrate that DFH-GAN can generate high-quality binary hash codes and exceed the performance of the state-of-the-art model greatly.

Force Banner for the Recognition of Spatial Relations

Robin Deléarde, Camille Kurtz, Laurent Wendling, Philippe Dejean

Auto-TLDR; Spatial Relation Recognition using Force Banners

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Studying the spatial organization of objects in images is fundamental to increase both the understanding of the sensed scene and the accuracy of the perceived similarity between images. This often leads to the problem of spatial relation recognition: given two objects depicted in an image, what is their spatial relation? In this article, we consider this as a classification problem. Instead of considering directly the original image space (or imaging features) to predict the spatial relation, we propose a novel intermediate representation (called Force Banner) modeling rich spatial information between pairs of objects composing a scene. Such a representation captures the relative position between objects using a panel of forces (attraction and repulsion), that take into account the structural shapes of the objects and their distance in a directional fashion. Force Banners are used to feed a classical 2D Convolutional Neural Network (CNN) for the recognition of spatial relations, benefiting from pre-trained models and fine-tuning. Experimental results obtained on a dataset of images with various shapes highlight the interest of this approach, and in particular its benefit to describe spatial information.

Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation

Dimche Kostadinov, Davide Scarammuza

Auto-TLDR; Unsupervised Representation Learning from Local Event Data for Pattern Recognition

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Event-based cameras record asynchronous streamof per-pixel brightness changes. As such, they have numerous advantages over the common frame-based cameras, including high temporal resolution, high dynamic range, and no motion blur. Due to the asynchronous nature, efficient learning of compact representation for event data is challenging. While the extend to which the spatial and temporal event "information" is useful for pattern recognition tasks is not fully explored. In this paper, we focus on single layer architectures. We analyze the performance of two general problem formulations,i.e., the direct and the inverse, for unsupervised feature learning from local event data,i.e., local volumes of events that are described in space and time. We identify and show the main advantages of each approach. Theoretically, we analyze guarantees for local optimal solution, possibility for asynchronous and parallel parameter update as well as the computational complexity. We present numerical experiments for the task of object recognition, where we evaluate the solution under the direct and the inverse problem.We give a comparison with the state-of-the-art methods. Our empirical results highlight the advantages of the both approaches for representation learning from event data. Moreover, we show improvements of up to 9% in the recognition accuracy compared to the state-of-the-art methods from the same class of methods.

A Novel Deep-Learning Pipeline for Light Field Image Based Material Recognition

Yunlong Wang, Kunbo Zhang, Zhenan Sun

Auto-TLDR; Factorize-Connect-Merge Deep Learning Pipeline for Light Field Image Based Material Recognition

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The primitive basis of image based material recognition builds upon the fact that discrepancies in the reflectances of distinct materials lead to imaging differences under multiple viewpoints. LF cameras possess coherent abilities to capture multiple sub-aperture views (SAIs) within one exposure, which can provide appropriate multi-view sources for material recognition. In this paper, a unified ``Factorize-Connect-Merge`` (FCM) deep-learning pipeline is proposed to solve problems of light field image based material recognition. 4D light-field data as input is initially decomposed into consecutive 3D light-field slices. Shallow CNN is leveraged to extract low-level visual features of each view inside these slices. As to establish correspondences between these SAIs, Bidirectional Long-Short Term Memory (Bi-LSTM) network is built upon these low-level features to model the imaging differences. After feature selection including concatenation and dimension reduction, effective and robust feature representations for material recognition can be extracted from 4D light-field data. Experimental results indicate that the proposed pipeline can obtain remarkable performances on both tasks of single-pixel material classification and whole-image material segmentation. In addition, the proposed pipeline can potentially benefit and inspire other researchers who may also take LF images as input and need to extract 4D light-field representations for computer vision tasks such as object classification, semantic segmentation and edge detection.

Video Lightening with Dedicated CNN Architecture

Li-Wen Wang, Wan-Chi Siu, Zhi-Song Liu, Chu-Tak Li, P. K. Daniel Lun

Auto-TLDR; VLN: Video Lightening Network for Driving Assistant Systems in Dark Environment

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Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is useful as reference to indirectly lightening the environment, or for vision-based application systems, such as driving assistant systems. The VLN consists of several Lightening Back-Projection (LBP) and Temporal Aggregation (TA) blocks. Each LBP block enhances the low-light frame by domain transfer learning that iteratively maps the frame between the low- and normal-light domains. A TA block handles the motion among neighboring frames by investigating the spatial and temporal relationships. Several TAs work in a multi-scale way, which compensates the motions at different levels. The proposed architecture has a consistent enhancement for different levels of illuminations, which significantly increases the visual quality even in the extremely dark environment. Extensive experimental results show that the proposed approach outperforms other methods under both objective and subjective metrics.

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Imperial London, Sergiy Bokhnyak, Susan Walsh, Mark Shriver, Michael Bronstein, Peter Claes

Auto-TLDR; Multi-biometric Fusion for Biometric Verification using 3D Facial Mesures

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Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.

Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

Jianyang Qin, Lunke Fei, Shaohua Teng, Wei Zhang, Genping Zhao, Haoliang Yuan

Auto-TLDR; Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

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Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.

Fast Region-Adaptive Defogging and Enhancement for Outdoor Images Containing Sky

Zhan Li, Xiaopeng Zheng, Bir Bhanu, Shun Long, Qingfeng Zhang, Zhenghao Huang

Auto-TLDR; Image defogging and enhancement of hazy outdoor scenes using region-adaptive segmentation and region-ratio-based adaptive Gamma correction

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Inclement weather, haze, and fog severely decrease the performance of outdoor imaging systems. Due to a large range of the depth-of-field, most image dehazing or enhancement methods suffer from color distortions and halo artifacts when applied to real-world hazy outdoor scenes, especially those with the sky. To effectively recover details in both distant and nearby regions as well as to preserve color fidelity of the sky, in this study, we propose a novel image defogging and enhancement approach based on a replaceable plug-in segmentation module and region-adaptive processing. First, regions of the grayish sky, pure white objects, and other parts are separated. Several segmentation methods are studied, including an efficient threshold-based one used for this work. Second, a luminance-inverted multi-scale Retinex with color restoration (MSRCR) and region-ratio-based adaptive Gamma correction are applied to non-grayish and non-white areas. Finally, the enhanced regions are stitched seamlessly by using a mean-filtered region mask. The proposed method is efficient in defogging natural outdoor scenes and requires no training data or prior knowledge. Extensive experiments show that the proposed approach not only outperforms several state-of-the-art defogging methods in terms of both visibility and color fidelity, but also provides enhanced outputs with fewer artifacts and halos, particularly in sky regions.

Vision-Based Multi-Modal Framework for Action Recognition

Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini

Auto-TLDR; Multi-modal Framework for Human Activity Recognition Using RGB, Depth and Skeleton Data

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Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.

Modeling Extent-Of-Texture Information for Ground Terrain Recognition

Shuvozit Ghose, Pinaki Nath Chowdhury, Partha Pratim Roy, Umapada Pal

Auto-TLDR; Extent-of-Texture Guided Inter-domain Message Passing for Ground Terrain Recognition

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Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally. At first, the proposed method uses a CNN backbone feature extractor network to capture meaningful information of a ground terrain image, and model the extent of texture and shape information locally. Then, the order-less texture information and ordered shape information are encoded in a patch-wise manner, which is utilized by intra-domain message passing module to make every patch aware of each other for rich feature learning. Next, the Extent-of-Texture (EoT) Guided Inter-domain Message Passing module combines the extent of texture and shape information with the encoded texture and shape information in a patch-wise fashion for sharing knowledge to balance out the order-less texture information with ordered shape information. Further, Bilinear model generates a pairwise correlation between the order-less texture information and ordered shape information. Finally, the ground-terrain image classification is performed by a fully connected layer. The experimental results indicate superior performance of the proposed model over existing state-of-the-art techniques on publicly available datasets like DTD, MINC and GTOS-mobile.

Comparison of Deep Learning and Hand Crafted Features for Mining Simulation Data

Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Nan Pu, Wei Chen, Michael Lew

Auto-TLDR; Automated Data Analysis of Flow Fields in Computational Fluid Dynamics Simulations

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Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes, airplanes parts etc. The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated. Automated data analysis methods are warranted but a non-trivial obstacle is given by the very large dimensionality of the data. A flow field typically consists of six measurement values for each point of the computational grid in 3D space and time (velocity vector values, turbulent kinetic energy, pressure and viscosity). In this paper we address the task of extracting meaningful results in an automated manner from such high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data, i.e. predicting drag and lift forces applied on an airfoil. We also propose an adaptation of the classical hand crafted features known from computer vision to address the same problem and compare a large variety of descriptors and detectors. Finally, we compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches. Our results show that the deep learning-based methods, as well as hand crafted feature based approaches, are well-capable to accurately describe the content of the CFD simulation output on the proposed dataset.

Aggregating Object Features Based on Attention Weights for Fine-Grained Image Retrieval

Hongli Lin, Yongqi Song, Zixuan Zeng, Weisheng Wang

Auto-TLDR; DSAW: Unsupervised Dual-selection for Fine-Grained Image Retrieval

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Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using adaptive threshold method to perform feature selection on local and global activation map in turn. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

Multi-Scale Keypoint Matching

Sina Lotfian, Hassan Foroosh

Auto-TLDR; Multi-Scale Keypoint Matching Using Multi-Scale Information

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We propose a new hierarchical method to match keypoints by exploiting information across multiple scales. Traditionally, for each keypoint a single scale is detected and the matching process is done in the specific scale. We replace this approach with matching across scale-space. The holistic information from higher scales are used for early rejection of candidates that are far away in the feature space. The more localized and finer details of lower scale are then used to decide between remaining possible points. The proposed multi-scale solution is more consistent with the multi-scale processing that is present in the human visual system and is therefore biologically plausible. We evaluate our method on several datasets and achieve state of the art accuracy, while significantly outperforming others in extraction time.

Feature Extraction and Selection Via Robust Discriminant Analysis and Class Sparsity

Ahmad Khoder, Fadi Dornaika

Auto-TLDR; Hybrid Linear Discriminant Embedding for supervised multi-class classification

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The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multi-class classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Inter-class sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.

Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-To-Video Search

Savas Ozkan, Gözde Bozdağı Akar

Auto-TLDR; Fast and Robust Image-to-Video Retrieval Using Local and Global Descriptors

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Cost-effective visual representation and fast query-by-example search are two challenging goals hat should be provided for web-scale visual retrieval task on a moderate hardware. In this paper, we introduce a fast yet robust method that ensures both of these goals by obtaining the state-of-the-art results for an image-to-video search scenario. To this end, we present important enhancements to commonly used indexing and visual representation techniques by promoting faster, better and more moderate retrieval performance. We also boost the effectiveness of the method for visual distortion by exploiting the individual decision results of local and global descriptors in the query time. By this way, local content descriptors effectively represent copied / duplicated scenes with large geometric deformations, while global descriptors for near duplicate and semantic searches are more practical. Experiments are conducted on the large-scale Stanford I2V dataset. The experimental results show that the method is effective in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. In addition, the proposed method is fairly accurate and achieves state-of-the-art performance based on the mAP score of the dataset. Lastly, we report additional mAP scores after updating the ground annotations obtained by the retrieval results of the proposed method showing more clearly the actual performance.

Attentive Hybrid Feature Based a Two-Step Fusion for Facial Expression Recognition

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

Auto-TLDR; Attentive Hybrid Architecture for Facial Expression Recognition

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Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

Supervised Feature Embedding for Classification by Learning Rank-Based Neighborhoods

Ghazaal Sheikhi, Hakan Altincay

Auto-TLDR; Supervised Feature Embedding with Representation Learning of Rank-based Neighborhoods

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In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighborhoods. The notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. The two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. The hidden layer determines the reduced subspace for the train samples. The obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.

Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

Nina Weng, Jiahao Wang, Annan Li, Yunhong Wang

Auto-TLDR; 2S-TCN: A Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

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In the field of facial attractiveness prediction, while deep models using static pictures have shown promising results, little attention is paid to dynamic facial information, which is proven to be influential by psychological studies. Meanwhile, the increasing popularity of short video apps creates an enormous demand of facial attractiveness prediction from short video clips. In this paper, we target on the dynamic facial attractiveness prediction problem. To begin with, a large-scale video-based facial attractiveness prediction dataset (VFAP) with more than one thousand clips from TikTok is collected. A two-stream temporal convolutional network (2S-TCN) is then proposed to capture dynamic attractiveness feature from both facial appearance and landmarks. We employ attentive feature enhancement along with specially designed modality and temporal fusion strategies to better explore the temporal dynamics. Extensive experiments on the proposed VFAP dataset demonstrate that 2S-TCN has a distinct advantage over the state-of-the-art static prediction methods.

Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Akshay Punjabi, José Ramón Prieto Fontcuberta, Enrique Vidal

Auto-TLDR; Writer Recognition Using Deep Neural Networks for Handwritten Text Images

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Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all - a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.

Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning

Hyunseung Chung, Woo-Jeoung Nam, Seong-Whan Lee

Auto-TLDR; Robust Remote Sensing Image Retrieval Using Group Convolution with Attention Mechanism and Metric Learning

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Remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial Image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

Local Attention and Global Representation Collaborating for Fine-Grained Classification

He Zhang, Yunming Bai, Hui Zhang, Jing Liu, Xingguang Li, Zhaofeng He

Auto-TLDR; Weighted Region Network for Cosmetic Contact Lenses Detection

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The cosmetic contact lenses over an iris may change its original textural pattern that is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. Aiming at cosmetic contact lenses detection of practical application system, some approaches have been proposed but still facing unsolved problems, such as low quality iris images and inaccurate localized iris boundaries. In this paper, we propose a novel framework called Weighted Region Network (WRN) for the cosmetic contact lenses detection. The WRN includes both the local attention Weight Network and the global classification Region Network. With the inherent attention mechanism, the proposed network is able to find the most discriminative regions, which reduces the requirement for target detection and improves the ability of classification based on some specific areas and patterns. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

Mirco Planamente, Andrea Bottino, Barbara Caputo

Auto-TLDR; A Single Stream Architecture for Egocentric Action Recognition from the First-Person Point of View

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Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main actor's pose and thus tend to record only parts of the movement when focusing on manipulation tasks. Thus, the amount of information about the action itself is limited, making crucial the understanding of the manipulated objects and their context. Many previous works addressed this issue with two-stream architectures, where one stream is dedicated to modeling the appearance of objects involved in the action, and another to extracting motion features from optical flow. In this paper, we argue that learning features jointly from these two information channels is beneficial to capture the spatio-temporal correlations between the two better. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion prediction task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach.