模式识别与人工智能
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  2018, Vol. 31 Issue (9): 773-785    DOI: 10.16451/j.cnki.issn1003-6059.201809001
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Local Group Sparse Representation with Mixed l2/l1/2 Norm
LI Xiaobao1, GUO Lijun1, ZHANG Rong1, HONG Jinhua1
1.Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211

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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/l1/2-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 l1/2-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/l1/2 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 wordsPerson Re-identification      Sparse Representation      Group Sparse      Norm     
Received: 23 April 2018     
ZTFLH: TP 391  
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LI Xiaobao
GUO Lijun
ZHANG Rong
HONG Jinhua
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LI Xiaobao,GUO Lijun,ZHANG Rong等. Local Group Sparse Representation with Mixed l2/l1/2 Norm[J]. , 2018, 31(9): 773-785.
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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
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