Publication Type

Conference Proceeding Article

Publication Date

10-2015

Abstract

User-generated tags associated with social images are frequently imprecise and incomplete. Therefore, a fundamental challenge in tag-based applications is the problem of tag relevance estimation, which concerns how to interpret and quantify the relevance of a tag with respect to the contents of an image. In this paper, we address the key problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance estimation to directly optimize the ranking performance of tag-based image search. A supervision step is introduced into the neighbour voting scheme, in which tag relevance is estimated by accumulating votes from visual neighbours. Through explicitly modelling the neighbour weights and tag correlations, the risk of making heuristic assumptions is effectively avoided for conventional methods. Extensive experiments on a benchmark dataset in comparison with the state-of-the-art methods demonstrate the promise of our approach.

Keywords

Learning to rank, Neighbour voting, Tag relevance estimation, Tag-based image search

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

MM '15: Proceedings of the 23rd ACM International Conference on Multimedia: Brisbane, Australia, October 26-30, 2015

First Page

895

Last Page

898

ISBN

9781450334594

Identifier

10.1145/2733373.2806358

Publisher

ACM

City or Country

New York

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/10.1145/2733373.2806358

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