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Multi-view multi-sparsity kernel reconstruction for multi-class image classification.pdf

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Multi-view multi-sparsity kernel reconstruction for multi-class image classification.pdf

Neurocomputing 169 (2015) 43–49 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom Multi-view multi-sparsity kernel reconstruction for multi-class image classi?cation Xiaofeng Zhu a,b, Qing Xie c, Yonghua Zhu d, Xingyi Liu e, Shichao Zhang b,n a School of Mathematics and Statistics, Xian Jiaotong University, PR China b Guangxi Key Lab of Multi-source Information Mining Security, Guangxi Normal University, PR China c Division of CEMSE, KAUST, Saudi Arabia d School of Computer, Electronics and Information, Guangxi University, China e Qinzhou Institute of Socialism, Qinzhou, Guangxi, China article info Article history: Received 29 April 2014 Received in revised form 19 August 2014 Accepted 25 August 2014 Available online 28 May 2015 Keywords: Image classi?cation Multi-view classi?cation Sparse coding Structure sparsity Reproducing kernel Hilbert space abstract This paper addresses the problem of multi-class image classi?cation by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR ?rst maps them into a highdimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classi?cation rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms. 2015 Elsevier B.V. All rights reserved. 1. Introduction In image classi?cation, an image is often represented by its visual feature, such as HSV (Hue, Saturation, Value) color histogram, LBP (Local Binary Pattern), SIFT (Scale invariant feature transform), CENTRIST (CENsus TRansform hISTgram), and so on. Usually, different representations describe different characteristics of images. For example, CENTRIST