Social tag relevance estimation via ranking-oriented neighbour voting

Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review


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.

Original languageEnglish
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
Place of PublicationBrisbane, Australia
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Electronic)9781450334594
Publication statusPublished - 13 Oct 2015
Externally publishedYes
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: 26 Oct 201530 Oct 2015


Conference23rd ACM International Conference on Multimedia, MM 2015


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