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Learning a representation with the block-diagonal structure for pattern classification

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arXiv 2019年

作者： Yin, He-Feng Wu, Xiao-Jun Kittler, Josef Feng, Zhen-Hua School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The code will be released upon the acceptance of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： image recognition

Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

Multiple riemannian manifold-valued descriptors based image set classification with multi-kernel metric learning

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arXiv 2019年

作者： Wang, Rui Wu, Xiao-Jun Kittler, Josef School of Internet of Things Engineering Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

The importance of wild video based image set recognition is becoming monotonically increasing due to the large amount of video resources obtained by diversified video collection approaches, like surveillance, drive recorders, smart phones, and internet. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and feature extraction is a big challenge for set-based classification algorithms. In recent years, some proposed image set classification methods have made a considerable advance by modeling the original image set with covariance matrix, linear subspace, or Gaussian distribution. Moreover, the distinctive geometry spanned by them are Symmetric Positive Definite (SPD) manifold, Grassmann manifold, and Gaussian embedded Riemannian manifold, respectively. As a matter of fact, most of them just adopt a single geometric model to describe each given image set, which may lose some other useful information for classification. To tackle this problem, we propose a novel algorithm to model each image set from a multi-geometric perspective. Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously. In order to fuse these multiple heterogeneous Riemannian manifoldvalued features, the well-equipped Riemannian kernel functions are first utilized to map them into high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework is devised to embed the learned hybrid kernels into a lower dimensional common subspace for classification. We conduct experiments on four widely used datasets corresponding to four different classification tasks: video-based face recognition, set-based object categorization, video-based emotion recognition, and dynamic scene classification, to evaluate the classification performance of the proposed algorithm. Extensive experimental results justify its superiority over the state-of-the-art. Copyright © 2019, Th

关键词： Vector spaces

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

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arXiv 2019年

作者： Liu, Fanghui Gong, Chen Huang, Xiaolin Zhou, Tao Yang, Jie Tao, Dacheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers. Copyright © 2019, The Authors. All rights reserved.

关键词： Template matching

Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

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arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

Multi-Stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning

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Multi-Stage Suture Detection for Robot Assisted Anastomosis ...

IEEE International Conference on Robotics and Automation

作者： Yang Hu Yun Gu Jie Yang Guang-Zhong Yang Imperial College London Hamlyn Centre for Robotic Surgery UK School of Biomedical Engineering and Institute of Image Processing & Pattern Recognition Imperial College London UK

The technique of robust suture detection is vital in many applications including trainee suturing skill evaluation, suture augmentation in robotic-assisted surgery and suture recognition for automatic suturing. Due to the complicated environment of surgery, the detection of a suture is challenged by high deformation and frequent occlusion. In this paper, we propose a deep multi-stage framework for suture detection. The fully convolutional neural networks are firstly used to predict a gradient map which not only serves as a segmentation mask, but also provides useful structure information for the following thread centerline reconstruction. An overlapping map is also predicted to improve the quality of the gradient map in self-intersection area. Based on the gradient map, multiple segments of the thread are extracted and linked to form the whole thread using a curvilinear structure detector. Experiments on two types of threads demonstrate that the proposed method is able to detect the thread with human level performance when the thread is no occlusion or under finite self-intersection.

关键词： Yarn Instruction sets Surgery Splines (mathematics) image reconstruction Robots Task analysis

NCEM: Network structural similarity metric-based clustering for noisy cryo-EM single particle images

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NCEM: Network structural similarity metric-based clustering ...

Chinese Automation Congress

作者： Yin Shuo Biao Zhang Hong-Bin Shen Yang Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Computer Science Shanghai Jiao Tong University Shanghai China

Cryo-EM single particle image reconstruction is currently a powerful technique for revealing the structure of biomacromolecules. Compared to traditional structural biology techniques like X-Ray, it requires fewer restrictions on specimens and is highly efficient with image processing tools. In this single particle reconstruction protocol, the ultimate goal is to identify different particle projection orientations. Since the picked single particle images are highly noisy, clustering is an important step to refrain noise by dividing images with similar projection angles into groups and averaging these images. The goal of clustering analysis is to assign similar particles into same class, so similarity measurement between particles is an important part in all clustering algorithms. Directly measuring the similarity of two particle images will be unreliable due to their low SNR. In this study, we propose a novel network structural similarity metric-based clustering algorithm NCEM for clustering the single particle images. We first construct a complex network for all particle images, where each node represents a particle. Then calculating the similarity between two nodes using structural similarity. This new network-based single particle image similarity metric has advantages over direct measurement for its noise resistance by using the structural information of the network. Our experiments on both artificial and experimental datasets demonstrate its effectiveness.

关键词： Cryo-EM Single particle image clustering Complex network Structural similarity

Dual encoder-decoder based generative adversarial networks for disentangled facial representation learning

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arXiv 2019年

作者： Hu, Cong Feng, Zhen-Hua Wu, Xiao-Jun Kittler, Josef School of Internet of Things Engineering Jiangnan University Wuxi Jiangsu Province214122 China Jiangsu Provincial Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi Jiangsu Province214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

To learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimising the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Embedding bilateral filter in least squares for efficient edge-preserving image smoothing

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arXiv 2018年

作者： Liu, Wei Zhang, Pingping Chen, Xiaogang Shen, Chunhua Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Dalian University of Technology China University of Shanghai for Science and Technology China University of Adelaide Australia

Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods can yield superior performance over local ones. However, local methods usually run much faster than global ones. In this paper, we propose a new global method that embeds the bilateral filter in the least squares model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the bilateral filter and least squares model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the bilateral filter with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Nonlinear filtering