Social tag relevance learning via ranking-oriented neighbor voting
High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of c...
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sg-smu-ink.sis_research-45432020-04-01T06:34:29Z Social tag relevance learning via ranking-oriented neighbor voting CUI, Chaoran SHEN, Jialie MA, Jun LIAN, Tao High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of current search systems. To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively. In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance learning to directly optimize the ranking performance of tag-based image search. Specifically, a supervision step is introduced into the neighbor voting scheme, in which the tag relevance is estimated by accumulating votes from visual neighbors. Through explicitly modeling the neighbor weights and tag correlations, the risk of making heuristic assumptions is effectively avoided. Besides, our approach does not suffer from the scalability problem since a generic model is learned that can be applied to all tags. Extensive experiments on two benchmark datasets in comparison with the state-of-the-art methods demonstrate the promise of our approach. 2017-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3542 info:doi/10.1007/s11042-016-3512-1 https://ink.library.smu.edu.sg/context/sis_research/article/4543/viewcontent/SocialTagRelevanceLeranviaRankNeighborVoting_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Learning to rank Neighbor voting Tag relevance learning Tag-based image search Computer Sciences Databases and Information Systems |
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Learning to rank Neighbor voting Tag relevance learning Tag-based image search Computer Sciences Databases and Information Systems CUI, Chaoran SHEN, Jialie MA, Jun LIAN, Tao Social tag relevance learning via ranking-oriented neighbor voting |
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High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of current search systems. To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively. In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach to facilitate tag relevance learning to directly optimize the ranking performance of tag-based image search. Specifically, a supervision step is introduced into the neighbor voting scheme, in which the tag relevance is estimated by accumulating votes from visual neighbors. Through explicitly modeling the neighbor weights and tag correlations, the risk of making heuristic assumptions is effectively avoided. Besides, our approach does not suffer from the scalability problem since a generic model is learned that can be applied to all tags. Extensive experiments on two benchmark datasets in comparison with the state-of-the-art methods demonstrate the promise of our approach. |
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CUI, Chaoran SHEN, Jialie MA, Jun LIAN, Tao |
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CUI, Chaoran SHEN, Jialie MA, Jun LIAN, Tao |
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CUI, Chaoran |
title |
Social tag relevance learning via ranking-oriented neighbor voting |
title_short |
Social tag relevance learning via ranking-oriented neighbor voting |
title_full |
Social tag relevance learning via ranking-oriented neighbor voting |
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Social tag relevance learning via ranking-oriented neighbor voting |
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Social tag relevance learning via ranking-oriented neighbor voting |
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social tag relevance learning via ranking-oriented neighbor voting |
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Institutional Knowledge at Singapore Management University |
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2017 |
url |
https://ink.library.smu.edu.sg/sis_research/3542 https://ink.library.smu.edu.sg/context/sis_research/article/4543/viewcontent/SocialTagRelevanceLeranviaRankNeighborVoting_2016.pdf |
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