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 |
Published version: | http://dx.doi.org/10.1016/j.patcog.2013.04.008 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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RA_hdl_79474.pdf Restricted Access | Restricted Access | 10.07 MB | Adobe PDF | View/Open |
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