Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/79474
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wu, J. | - |
dc.contributor.author | Shen, H. | - |
dc.contributor.author | Li, Y. | - |
dc.contributor.author | Xiao, Z. | - |
dc.contributor.author | Lu, M. | - |
dc.contributor.author | Wang, C. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Pattern Recognition, 2013; 46(11):2927-2939 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.issn | 1873-5142 | - |
dc.identifier.uri | http://hdl.handle.net/2440/79474 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Jun Wu, Hong Shen, Yi-Dong Li, Zhi-Bo Xiao, Ming-Yu Lu and Chun-Li Wang | - |
dc.language.iso | en | - |
dc.publisher | Pergamon-Elsevier Science Ltd | - |
dc.rights | © 2013 Elsevier Ltd. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.patcog.2013.04.008 | - |
dc.title | Learning a hybrid similarity measure for image retrieval | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.patcog.2013.04.008 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shen, H. [0000-0002-3663-6591] [0000-0003-0649-0648] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RA_hdl_79474.pdf Restricted Access | Restricted Access | 10.07 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.