Conference Proceeding Article
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.
Learning to rank, Neighbour voting, Tag relevance estimation, Tag-based image search
Computer Sciences | Databases and Information Systems
Data Management and Analytics
MM '15: Proceedings of the 23rd ACM International Conference on Multimedia: Brisbane, Australia, October 26-30, 2015
City or Country
CUI, Chaoran; SHEN, Jialie; MA, Jun; and LIAN, Tao.
Social tag relevance estimation via ranking-oriented neighbour voting. (2015). MM '15: Proceedings of the 23rd ACM International Conference on Multimedia: Brisbane, Australia, October 26-30, 2015. 895-898. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3541
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.