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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 6727105, 11 pages
Research Article

Kernel-Based Multiview Joint Sparse Coding for Image Annotation

1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Haidian District, Beijing 100876, China
2School of Electronics and Information Engineering, North China University of Technology, No. 5, Jinyuanzhang Road, Shijingshan District, Beijing 100144, China
3School of Computer Science, North China University of Technology, No. 5, Jinyuanzhang Road, Shijingshan District, Beijing 100144, China

Correspondence should be addressed to Miao Zang

Received 28 October 2016; Accepted 11 January 2017; Published 19 March 2017

Academic Editor: Wanquan Liu

Copyright © 2017 Miao Zang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal sparse representations and discriminative dictionaries adaptively, which can effectively employ the complementary information of different views. An optimization algorithm is presented by extending K-singular value decomposition (KSVD) and accelerated proximal gradient (APG) algorithms to the kernel multiview framework. In addition, a label propagation scheme using the sparse reconstruction and weighted greedy label transfer algorithm is also proposed. Comparative experiments on three datasets have demonstrated the competitiveness of proposed approach compared with other related methods.