混合l<sub>2</sub>/l<sub>1/2</sub>范数的局部组稀疏表示方法

doi:10.16451/j.cnki.issn1003-6059.201809001

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1774 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

In the existing person re-identification approaches based on sparse representation, l₁ regularization is generally utilized to approximate l₀-norm sparsity. Under the restricted isometry property (RIP) conditions, the l₀ regularization is equivalent to the l₁ regularization. However, it is difficult to meet the RIP conditions in person re-identification with many disturbing factors, such as cluttered background and object occlusion. In this paper, a group sparse representation method with mixed l₂/l_1/2-norm is proposed. The identical person image sequence in the gallery is regarded as a group, the intra-group structure is constrained by l₂-norm, and the inter-group structure is constrained by l_1/2-norm. The resulting model is more robust to the occlusion and cluttered backgrounds. The human body structure constraint is introduced to further enhance the discriminability of the proposed model. The person image is divided into several neighboring block regions. An adaptive sparse model of mixed l₂/l_1/2 norm is constructed for each region. Finally, the several sparse models are merged to identify persons. Experiments on PRID 2011 and iLIDS-VID datasets verify the effectiveness of the proposed model.

Key words： Person Re-identification Sparse Representation Group Sparse Norm

Received: 23 April 2018

ZTFLH:

TP 391

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LI Xiaobao
	GUO Lijun
	ZHANG Rong
	HONG Jinhua

Cite this article:

LI Xiaobao,GUO Lijun,ZHANG Rong等. Local Group Sparse Representation with Mixed l₂/l_1/2 Norm[J]. , 2018, 31(9): 773-785.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201809001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I9/773

[1] ZHAO R,OUYANG W L,WANG X G. Unsupervised Salience Learning for Person Re identification // Proc of the IEEE Confe rence on Computer Vision and Pattern Recognition.Washington, USA: IEEE, 2013: 3586-3593.
[2] FARENZENA M, BAZZANI L, PERINA A,et al.Person Re id entification by Symmetry Driven Accumulation of Local Features // Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington, USA: IEEE, 2010: 2360-2367..
[3] LIAO S C, HU Y, ZHU X Y,et al. Person Re identification by Local Maximal Occurrence Representation and Metric Learning // Proc of the IEEE Conference on Computer Vision and Pattern Re cognition . Washington, USA: IEEE, 2015: 2197-2206..
[4] GUILLAUMIN M, VERBEEK J, SCHMID C. Is That You? Metric Learning Approaches for Face Identification // Proc of the 12th IEEE International Conference on Computer Vision.Washington, USA: IEEE, 2009: 498-505..
[5] PROSSER B, ZHANG W C, GONG S G,et al.Person Re identification by Support Vector Ranking // Proc of the British Machine Vision Conference. London, UK: BMVA, 2010. DOI: 10.5244/C.24.21..
[6] WANG T Q, GONG S G, ZHU X T,et al.Person Re identification by Video Ranking // Proc of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 688-703..
[7] SIMONNET D, LEWANDOWSKI M, VELASTIN S A,et al. Re identification of Pedestrians in Crowds Using Dynamic Time Warping // Proc of the European Conference on Computer Vision. Florence, Italy: Springer, 2012: 423-432..
[8] NIXON M S, TAN T N, CHELLAPPA R. Human Identification Based on Gait. Majlesi Journal of Electrical Engineering, 2008,2(3): 15-23..
[9] WRIGHT J, YANG A Y, GANESH A,et al. Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[10] AGARWAL S, ROTH D. Learning a Sparse Representation for Object Detection // Proc of the 7th European Conference on Computer Vision. Copenhagen, Denmark: Springer, 2002: 113-130.
[11] KHEDHER M I, EL YACOUBI M A, DORIZZI B. Multi shot SURF Based Person Re identification via Sparse Representation // Proc of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. Washington, USA: IEEE, 2013: 159-164.
[12] LISANTI G, MASI I, BAGDANOV A D,et al.Person Re identification by Iterative Re weighted Sparse Ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8): 1629-1642.
[13] KARANAM S, LI Y, RADKE R J. Person Re identification with Block Sparse Recovery. Image and Vision Computing, 2017, 60: 75-90.
[14] AN L, CHEN X J, YANG S F,et al. Sparse Representation Ma tching for Person Re identification. Information Sciences, 2016, 355/356: 74-89.
[15] XU Z B, ZHANG H, WANG Y,et al.L1/2 Regularization. Science China(Information Sciences), 2010, 53(6): 1159-1169.
[16] TIBSHIRANI R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society(Methodological), 1996, 58(1): 267-288.
[17] DONOHO D L, HUO X. Uncertainty Principles and Ideal Atomic Decomposition. IEEE Transactions on Information Theory, 2001, 47(7): 2845-2862.
[18] ELAD M, BRUCKSTERIN A M. A Generalized Uncertainty Principle and Sparse Representation in Pairs of Bases. IEEE Transactions on Information Theory, 2002, 48(9): 2558-2567.
[19] DENG W, YIN W T, ZHANG Y. Group Sparse Optimization by Alternating Direction Method. Technical Report, TR11-06. Houston, USA: Rice University Computational and Applied Mathema tics , 2011.
[20] PARIKH N, BOYD S. Proximal Algorithms. Foundations & Tr ends in Optimization, 2014, 1(3): 123-231.
[21] BAK S, CARR P. Person Re identification Using Deformable Pa tch Metric Learning // Proc of the IEEE Winter Conference on Applications of Computer Vision.New York, USA: IEEE, 2016: 1-9.
[22] XIONG F, GOU M R, CAMPS O,et al.Person Re identification Using Kernel Based Metric Learning Methods // Proc of the European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 1-16.
[23] SUGIYAMA M. Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction // Proc of the 23rd International Confe rence on Machine Learning. New York, USA: ACM, 2006: 905-912.
[24] HIRZER M, BELEZNAI C, ROTH P M,et al. Person Re identification by Descriptive and Discriminative Classification // Proc of the Scandinavian Conference on Image Analysis. Berlin, Germany: Springer, 2011: 91-102.
[25] MATSUKAWA T, OKABE T, SUZUKI E,et al.Hierarchical Gaussian Descriptor for Person Re identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1363-1372.
[26] KARANAM S, WU Z, RADKE R J. Learning Affine Hull Representations for Multi shot Person Re identification [J/OL]. [2018-03-25]. https://ieeexplore.ieee.org/stamp/stamp.jsp？
[27] ZHENG A H, ZHANG X H, JIANG B,et al.A Subspace Learn-ing Approach to Multishot Person Reidentification [J/OL]. [2018-03-25]. https://ieeexplore.ieee.org/stamp/stamp.jsp？
[28] KARANAM S, GOU M, WU Z Y,et al.A Systematic Evaluation and Benchmark for Person Re identification: Features, Metrics, and Datasets [J/OL]. [2018-03-25]. https://arxiv.org/pdf/1605.09653v3.pdf.
[29] LI Y, WU Z Y, KARANAM S,et al. Multi shot Human Re identification Using Adaptive Fisher Discriminant Analysis // Proc of the British Machine Vision Conference. London, UK: BMVA, 2015. DOI: 10.5244/C.19.73.
[30] KARANAM S, LI Y, RADKE R J. Person Re identification with Discriminatively Trained Viewpoint Invariant Dictionaries // Proc of the IEEE International Conference on Computer Vision. Wa shington , USA: IEEE, 2015: 4516-4524.