Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/79474
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Type: Journal article
Title: Learning a hybrid similarity measure for image retrieval
Author: Wu, J.
Shen, H.
Li, Y.
Xiao, Z.
Lu, M.
Wang, C.
Citation: Pattern Recognition, 2013; 46(11):2927-2939
Publisher: Pergamon-Elsevier Science Ltd
Issue Date: 2013
ISSN: 0031-3203
1873-5142
Statement of
Responsibility: 
Jun Wu, Hong Shen, Yi-Dong Li, Zhi-Bo Xiao, Ming-Yu Lu and Chun-Li Wang
Abstract: Learning similarity measure from relevance feedback has become a promising way to enhance the image retrieval performance. Existing approaches mainly focus on taking short-term learning experience to identify a visual similarity measure within a single query session, or applying long-term learning methodology to infer a semantic similarity measure crossing multiple query sessions. However, there is still a big room to elevate the retrieval effectiveness, because little is known in taking the relationship between visual similarity and semantic similarity into account. In this paper, we propose a novel hybrid similarity learning scheme to preserve both visual and semantic resemblance by integrating short-term with long-term learning processes. Concretely, the proposed scheme first learns a semantic similarity from the users' query log, and then, taking this as prior knowledge, learns a visual similarity from a mixture of labeled and unlabeled images. In particular, unlabeled images are exploited for the relevant and irrelevant classes differently and the visual similarity is learned incrementally. Finally, a hybrid similarity measure is produced by fusing the visual and semantic similarities in a nonlinear way for image ranking. An empirical study shows that using hybrid similarity measure for image retrieval is beneficial, and the proposed algorithm achieves better performance than some existing approaches. © 2013 Elsevier Ltd.
Rights: © 2013 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.patcog.2013.04.008
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Computer Science publications

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