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Kernel-Based Multiview Joint Sparse Coding for Image Annotation
Joint Authors
Zang, Miao
Xu, Huimin
Zhang, Yongmei
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zang, Miao& Xu, Huimin& Zhang, Yongmei. 2017. Kernel-Based Multiview Joint Sparse Coding for Image Annotation. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1191465
Modern Language Association (MLA)
Zang, Miao…[et al.]. Kernel-Based Multiview Joint Sparse Coding for Image Annotation. Mathematical Problems in Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1191465
American Medical Association (AMA)
Zang, Miao& Xu, Huimin& Zhang, Yongmei. Kernel-Based Multiview Joint Sparse Coding for Image Annotation. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1191465
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191465