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Image categorization using non-negative kernel sparse representation
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DOI：10.1016/j.neucom.2016.08.144.png)
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Abstract
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Sparse representation of signals have become an important tool in computer vision. In many computer vision applications such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performances. Sparse representation models often contain two stages: sparse coding and dictionary learning. In this paper, we propose a non-linear non-negative sparse representation model: NNK-KSVD. In the sparse coding stage, a non-linear update rule is proposed to obtain the sparse matrix. In the dictionary learning stage, the proposed model extends the kernel KSVD by embedding the non-negative sparse coding. The proposed non-negative kernel sparse representation model was evaluated on several public image datasets for the task of classification. Experimental results show that by exploiting the non-linear structure in images and utilizing the 'additive' nature of non negative sparse coding, promising classification performance can be obtained. Moreover, the proposed sparse representation method was also evaluated in image retrieval tasks, competitive results were obtained. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Non-negative sparse coding
Kernel methods
Dictionary learning
Image classification
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