Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification
Online Publication Date2015-05-29
Print Publication Date2015-12
Permanent link to this recordhttp://hdl.handle.net/10754/556097
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AbstractThis 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.
CitationMulti-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification 2015 Neurocomputing