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

https://dx.doi.org/10.1109/TIP.2016.2522298

ISSN (print)

10577149

ISSN (electronic)

19410042

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Mathematics Commons

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