Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

12-2011

Abstract

Tag-based social image search has attracted great interest and how to order the search results based on relevance level is a research problem. Visual content of images and tags have both been investigated. However, existing methods usually employ tags and visual content separately or sequentially to learn the image relevance. This paper proposes a tag-based image search with visual-text joint hypergraph learning. We simultaneously investigate the bag-of-words and bag-of-visual-words representations of images and accomplish the relevance estimation with a hypergraph learning approach. Each textual or visual word generates a hyperedge in the constructed hypergraph. We conduct experiments with a real-world data set and experimental results demonstrate the effectiveness of our approach.

Keywords

hypergraph learning, tag-based image search, visual-text

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

MM '11: Proceedings of the 2011 ACM Multimedia Conference: November 28 - December 1, 2011, Scottsdale, AZ, USA

First Page

1517

Last Page

1520

ISBN

9781450306164

Identifier

10.1145/2072298.2072054

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2072298.2072054

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