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sparse representation and Collaborative representation? Both Help Image classification

IEEE ACCESS 2019年 7卷 76061-76070页

作者： Xie, Wen-Yang Liu, Bao-Di Shao, Shuai Li, Ye Wang, Yan-Jiang China Univ Petr Huadong Coll Informat & Control Engn Qingdao 266580 Shandong Peoples R China Qilu Univ Technol Shandong Comp Sci Ctr Shandong Prov Key Lab Comp Networks Jinan 250353 Shandong Peoples R China

Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative representation-based classification. However, the available representation-based methods still suffer from some problems. Especially, most methods only consider the shared representation of a test image. In this paper, we propose an elastic-net regularized regression algorithm (ENRR) for image classification. Specifically, our proposed method combines shared sparse representation with class specific collaborative representation when representing the test sample. Moreover, we extend the proposed ENRR to arbitrary kernel space to achieve better classification performance due to specificities and complexities of original images. The extensive experiments on face recognition datasets, handwritten recognition datasets, and remote sensing image datasets clearly demonstrate that the proposed ENRR outperforms several conventional methods in classification accuracy.

关键词： Collaborative representation based classification elastic-net regularized image classification kernel space sparse representation based classification

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IMPROVED COMBINATION OF LBP AND sparse representation based classification (SRC) FOR FACE RECOGNITION

IMPROVED COMBINATION OF LBP AND SPARSE REPRESENTATION BASED ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Min, Rui Dugelay, Jean-Luc EURECOM Dept Multimedia Commun Sophia Antipolis France

ISBN: (纸本)9781612843490

Recently, local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become both eminent techniques in face recognition. Preliminary techniques of combining LBP and SRC have been proposed in the literature. However, the state-of-art method suffers from the "curse of dimensionality" for real world scenarios. In this paper, a novel face recognition algorithm of combining LBP with SRC is proposed;in which the dimensionality problem is resolved by divide-and-conquer and the discriminative power is strengthen via its pyramid architecture. The proposed face recognition method is evaluated on AR Face Database and yields very impressive results.

关键词： Face Recognition Local Binary Patterns sparse representation based classification Divide and Conquer Pyramid Architecture

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EFFICIENT MULTI-DOMAIN DICTIONARY LEARNING WITH GANS 7

EFFICIENT MULTI-DOMAIN DICTIONARY LEARNING WITH GANS

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7th IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP)

作者： Wu, Cho Ying Neumann, Ulrich Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA

ISBN: (纸本)9781728127231

In this paper, we propose the multi-domain dictionary learning (MDDL) to make dictionary learning-based classification more robust to data representing in different domains. We use adversarial neural networks to generate data in different styles, and collect all the generated data into a miscellaneous dictionary. To tackle the dictionary learning with many samples, we compute the weighting matrix that compress the miscellaneous dictionary from multi-sample per class to single sample per class. We show that the time complexity solving the proposed MDDL with weighting matrix is the same as solving the dictionary with single sample per class. Moreover, since the weighting matrix could help the solver rely more on the training data, which possibly lie in the same domain with the testing data, the classification could be more accurate.

关键词： Dictionary learning sparse representation based classification multi-domain image classification

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Hierarchical sparse coding from a Bayesian perspective

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NEUROCOMPUTING 2018年 272卷 279-293页

作者： Zhang, Yupei Xiang, Ming Yang, Bo Xi An Jiao Tong Univ Dept Comp Sci & Technol Sch Elect & Informat Engn Xian Shaanxi Peoples R China

We consider the problem of hierarchical sparse coding, where not only a few groups of atoms are active at a time but also each group enjoys internal sparsity. The current approaches are usually to achieve between-group sparsity using the l(1) penalty, such that many groups have small coefficients rather than being accurately zeroed out. The trivial groups may incur the proneness to overfitting of noise and are thereby harmful to interpretability of sparse representation. To this end, we in this paper reformulate the hierarchical sparse model from a Bayesian perspective employing twofold priors: the spike-and-slab prior and the Laplacian prior. The former is utilized to explicitly induce between-group sparsity, while the latter is adopted for both inducing within-group sparsity and obtaining a small reconstruction error. We propose a nest prior by integrating the both priors to result in hierarchical sparsity. The resultant optimization problem can be delivered a convergence solution in a few iterations via the proposed nested algorithm, corresponding to the nested prior. In experiments, we evaluate the performance of our method on signal recovery, image inpainting and sparse representation based classification, with simulated signals and two publicly available image databases. The results manifest that the proposed method, compared with the popular methods for sparse coding, can yield more concise representation and more reliable interpretation of data. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Hierarchical sparse modeling Bayesian framework Spike-and-slab prior Laplacian prior sparse representation based classification

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Face Recognition: A sparse representation-based classification Using Independent Component Analysis

Face Recognition: A Sparse Representation-based Classificati...

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International Symposium on Telecommunications

作者： Mirhossein Mousavi Karimi Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781467320726

In this paper, we will describe a new method based on sparse representation-based classification (SRC) for face recognition. We have used histogram equalization as a preprocessing method in order to overcome the illumination variation problem. Using Independent Component Analysis we have obtained a feature vector for each face image which is robust to illumination variations and occlusion. Although SRC is robust against occlusion, it is rather slow. By using features with smaller dimensions but enough information, we can obtain better recognition rates in shorter periods. This method was tested on Extended Yale B database and obtained the recognition rates of 98.51% and 95.77% in presence of 10% and 20% occlusion, respectively.

关键词： Face Recognition ICA sparse representation based classification

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Combination of LBP and ESRC for Single Sample Infrared and Visible Face Fusion Recognition

Combination of LBP and ESRC for Single Sample Infrared and V...

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International Conference on Image and Video Processing, and Artificial Intelligence (IVPAI)

作者： Xie, Zhihua Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang Jiangxi Peoples R China

ISBN: (数字)9781510623118

ISBN: (纸本)9781510623118

Near infrared and visible fusion recognition is an active topic for robust face recognition. Local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become two significant techniques in face recognition. In this paper, near infrared and visible face fusion recognition based on LBP and extended SRC is proposed for single sample problem. Firstly, the local features are extracted by LBP descriptor for infrared and visible face representation. Secondly, the extend SRC (ESRC) is applied for single sample problem. Finally, to get a robust and time-efficient fusion model for unconstrained face recognition with single sample situation, the infrared and visible features fusion problem is resolved by error-level fusion based on ESRC. Experiments are performed on HITSZ LAB2 database and the experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition with single sample situation.

关键词： Local binary patterns sparse representation based classification Error-level fusion Face fusion recognition

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GBU based Face Recognition Techniques: A Review 4

GBU Based Face Recognition Techniques: A Review

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4th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Jayachitra, J. Devi, K. Suganya Vaiyshnavi, M. P. Srinivasan, P. IFET Coll Engn Dept IT Villupuram India Univ Coll Engn Dept CSE Panruti India Univ Coll Engn Dept Phys Panruti India

ISBN: (纸本)9781509045594

A large number of face recognition algorithms have been developed in last decades. Over the past four decades, performance of Face Recognition on frontal faces in controlled environment has improved significantly but frontal faces with uncontrolled environment and expression remains a challenge. The GBU based Face Recognition Techniques focuses on the attention of the fundamental problem of comparing single frontal face images across changes in appearance. This paper introduces many of the recent success and future challenges that are faced by the researchers in GBU based Face recognition as well as techniques used so far.

关键词： Local Region Principle Component Analysis Cohort Linear Discriminant Analysis Partial Least Square Regression sparse representation based classification

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Structured occlusion coding for robust face recognition

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NEUROCOMPUTING 2016年 178卷 11-24页

作者： Wen, Yandong Liu, Weiyang Yang, Meng Fu, Yuli Xiang, Youjun Hu, Rui S China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace l(1) norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： sparse representation based classification Occlusion dictionary Occlusion mask estimating Locality constrained dictionary Structured sparsity

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sparse discriminative feature weights learning

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NEUROCOMPUTING 2016年第Part3期173卷 1936-1942页

作者： Yan, Hui Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

sparse representation, a locality-based data representation method, leads to promising results in many scientific and engineering fields. Meanwhile in the study of feature selection, locality preserving is widely recognized as an effective measurement criterion. In this paper, we introduce l(1)-norm driven sparse representation into feature selection, and propose a novel joint feature weights learning algorithm, named sparse discriminative feature weights (SDFW). SDFW assigns the highest score to the feature that has the smallest difference between within-class reconstruction residual and between-class reconstruction residual in the space of selected features. It possesses the following advantages: (1) compared with feature selection methods based on k nearest neighbors, SDFX/V automatically (vs. manually) determines neighborhood for individual sample;(2) compared with conventional heuristic feature search which selects features individually, SDFW selects feature subset in batch mode. Extensive experiments on different data types demonstrate the effectiveness of SDFW. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Feature weights leaning sparse representation based classification Discriminant learning

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Global linear regression coefficient classifier for recognition

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OPTIK 2015年第21期126卷 3234-3239页

作者： Feng, Qingxiang Zhu, Xingjie Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Innovat Informat Ind Res Ctr Shenzhen Peoples R China China Hua Rang Holdings Corp LTD Dev Ctr Beijing Peoples R China

In this paper, a novel classifier based on linear regression classification (LRC), called global linear regression coefficient (GLRC) classifier, is proposed for recognition. LRC classifier uses the test sample and the class subspace to calculate the distance which will be used for classification. GLRC classifier uses the test sample vector and whole train space (all the class subspaces) to calculate the global linear regression coefficient. Then GLRC computes the signed square sum of the linear regression coefficients belonging to the same class, and the result will be used for classification. A large number of experiments on Yale face database and AR face database are used to evaluate the proposed algorithm. The experimental results demonstrate that the proposed method achieves better recognition rate than LRC classifier, sparse representation based classification (SRC) classifier, Collaborative representation based classification (CRC) classifier and two phase test sample sparse representation (TPTSSR) classifier and so on. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Linear regression classification sparse representation based classification Collaborative representation based classification

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