Image categorization using non-negative kernel sparse representation

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...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 269; pp. 21 - 28
Main Authors: Zhang, Yungang, Xu, Tianwei, Ma, Jieming
Format: Journal Article
Language:English
Published: Elsevier B.V 20.12.2017
Subjects:
Dictionary learning
Image classification
Kernel methods
Non-negative sparse coding
Kernel methods
Non-negative sparse coding
Dictionary learning
Image classification
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.08.144