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

Xiaofeng Zhu

doi:10.1016/j.neucom.2014.08.106

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

2015-12-01

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DOI：10.1016/j.neucom.2014.08.106

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This paper addresses the problem of multi-class image classification 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 first maps them into a high-dimensional 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 classification 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. (C) 2015 Elsevier B.V. All rights reserved.

Keywords:

Image classification

Multi-view classification

Sparse coding

Structure sparsity

Reproducing kernel Hilbert space

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

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