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.
| Original language | English |
|---|---|
| Title of host publication | MM 2015 - Proceedings of the 2015 ACM Multimedia Conference |
| Place of Publication | Brisbane, Australia |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 895-898 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450334594 |
| DOIs | |
| Publication status | Published - 13 Oct 2015 |
| Externally published | Yes |
| Event | 23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia Duration: 26 Oct 2015 → 30 Oct 2015 |
Conference
| Conference | 23rd ACM International Conference on Multimedia, MM 2015 |
|---|---|
| Country | Australia |
| City | Brisbane |
| Period | 26/10/15 → 30/10/15 |
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Social tag relevance estimation via ranking-oriented neighbour voting. / Cui, Chaoran; Shen, Jialie; Ma, Jun; Lian, Tao.
MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. Brisbane, Australia : Association for Computing Machinery, Inc, 2015. p. 895-898.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › Research › peer-review
TY - GEN
T1 - Social tag relevance estimation via ranking-oriented neighbour voting
AU - Cui, Chaoran
AU - Shen, Jialie
AU - Ma, Jun
AU - Lian, Tao
PY - 2015/10/13
Y1 - 2015/10/13
N2 - 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.
AB - 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.
KW - Learning to rank
KW - Neighbour voting
KW - Tag relevance estimation
KW - Tag-based image search
UR - http://www.scopus.com/inward/record.url?scp=84962821192&partnerID=8YFLogxK
U2 - 10.1145/2733373.2806358
DO - 10.1145/2733373.2806358
M3 - Conference Paper published in Proceedings
SP - 895
EP - 898
BT - MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
CY - Brisbane, Australia
ER -