Document Type
Journal Article
Department/Unit
Department of Mathematics
Language
English
Abstract
In this paper, we propose and develop a multi-visual-concept ranking (MultiVCRank) scheme for image retrieval. The key idea is that an image can be represented by several visual concepts, and a hypergraph is built based on visual concepts as hyperedges, where each edge contains images as vertices to share a specific visual concept. In the constructed hypergraph, the weight between two vertices in a hyperedge is incorporated, and it can be measured by their affinity in the corresponding visual concept. A ranking scheme is designed to compute the association scores of images and the relevance scores of visual concepts by employing input query vectors to handle image retrieval. In the scheme, the association and relevance scores are determined by an iterative method to solve limiting probabilities of a multi-dimensional Markov chain arising from the constructed hypergraph. The convergence analysis of the iteration method is studied and analyzed. Moreover, a learning algorithm is also proposed to set the parameters in the scheme, which makes it simple to use. Experimental results on the MSRC, Corel, and Caltech256 data sets have demonstrated the effectiveness of the proposed method. In the comparison, we find that the retrieval performance of MultiVCRank is substantially better than those of HypergraphRank, ManifoldRank, TOPHITS, and RankSVM.
Keywords
Visualization, Image retrieval, Algorithm design and analysis, Machine learning, Search problems, Feature extraction, Semantics
Publication Date
3-2016
Source Publication Title
IEEE Transactions on Image Processing
Volume
25
Issue
3
Start Page
1396
End Page
1409
Publisher
Institute of Electrical and Electronics Engineers
Peer Reviewed
1
Copyright
© © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI
10.1109/TIP.2016.2522298
Link to Publisher's Edition
ISSN (print)
10577149
ISSN (electronic)
19410042
Recommended Citation
Li, Xutao, Yunming Ye, and Michael K. Ng. "MultiVCRank with applications to image retrieval." IEEE Transactions on Image Processing 25.3 (2016): 1396-1409.