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Multi-view low-rank dictionary learning for image classification.pdf

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Multi-view low-rank dictionary learning for image classification.pdf

Pattern Recognition 50 (2016) 143–154 Contents lists available at ScienceDirect Pattern Recognition journal homepage: /locate/pr Multi-view low-rank dictionary learning for image classi?cation Fei Wu a,b, Xiao-Yuan Jing a,b,n, Xinge You c, Dong Yue a, Ruimin Hu b, Jing-Yu Yang d a School of Automation, Nanjing University of Posts and Telecommunications, China b School of Computer, Wuhan University, China c School of Electronic Information and Communications, Huazhong University of Science and Technology, China d College of Computer Science, Nanjing University of Science and Technology, China article info Article history: Received 17 October 2014 Received in revised form 25 May 2015 Accepted 13 August 2015 Available online 21 August 2015 Keywords: Multi-view dictionary learning Multi-view dictionary low-rank regularization Structural incoherence constraint Collaborative representation based classi?cation abstract Recently, a multi-view dictionary learning (DL) technique has received much attention. Although some multi-view DL methods have been presented, they suffer from the problem of performance degeneration when large noise exists in multiple views. In this paper, we propose a novel multi-view DL approach named multi-view low-rank DL (MLDL) for image classi?cation. Speci?cally, inspired by the low-rank matrix recovery theory, we provide a multi-view dictionary low-rank regularization term to solve the noise problem. We further design a structural incoherence constraint for multi-view DL, such that redundancy among dictionaries of different views can be reduced. In addition, to enhance ef?ciency of the classi?cation procedure, we design a classi?cation scheme for MLDL, which is based on the idea of collaborative representation based classi?cation. We apply MLDL for face recognition, object classi?cation and digit classi?cation tasks. Experimental results demonstrate the effectiveness and ef?ciency of the proposed approach. 2015 Elsevier Ltd. All rights reserved. 1