Kernel-Based Multiview Joint Sparse Coding for Image Annotation

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 fea...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2017; H. 2017; S. 1 - 11
Hauptverfasser: Zang, Miao, Zhang, Yongmei, Xu, Huimin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Annotations
Classification
Coding
Colleges & universities
Greedy algorithms
Hardware reviews
Image annotation
Image coding
Image retrieval
Kernels
Labels
Learning
Mathematical problems
Methods
Optimization
Parameter estimation
Semantics
Singular value decomposition
Sparsity
Visual discrimination
Visual tasks
ISSN:1024-123X, 1563-5147
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Zusammenfassung: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2017/6727105