Contextual Distance Refining for Image Retrieval

Handle URI:
http://hdl.handle.net/10754/344331
Title:
Contextual Distance Refining for Image Retrieval
Authors:
Islam, Almasri
Abstract:
Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Contextual Distance Refining for Image Retrieval 2014, 8 (1):40 The Open Cybernetics & Systemics Journal
Publisher:
Bentham Science Publishers Ltd.
Journal:
The Open Cybernetics & Systemics Journal
Issue Date:
16-Sep-2014
DOI:
10.2174/1874110X01408010040
Type:
Article
ISSN:
1874-110X
Sponsors:
This work was supported by Chongqing Key Laboratory of Computational Intelligence (Grant No. CQ-LCI-2013-02).
Additional Links:
http://benthamopen.com/openaccess.php?tocsj/articles/V008/40TOCSJ.htm
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorIslam, Almasrien
dc.date.accessioned2015-02-10T07:57:06Z-
dc.date.available2015-02-10T07:57:06Z-
dc.date.issued2014-09-16en
dc.identifier.citationContextual Distance Refining for Image Retrieval 2014, 8 (1):40 The Open Cybernetics & Systemics Journalen
dc.identifier.issn1874-110Xen
dc.identifier.doi10.2174/1874110X01408010040en
dc.identifier.urihttp://hdl.handle.net/10754/344331en
dc.description.abstractRecently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.en
dc.description.sponsorshipThis work was supported by Chongqing Key Laboratory of Computational Intelligence (Grant No. CQ-LCI-2013-02).en
dc.language.isoenen
dc.publisherBentham Science Publishers Ltd.en
dc.relation.urlhttp://benthamopen.com/openaccess.php?tocsj/articles/V008/40TOCSJ.htmen
dc.rightsThis is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.en
dc.subjectContextual distanceen
dc.subjectimage distanceen
dc.subjectimage retrievalen
dc.titleContextual Distance Refining for Image Retrievalen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe Open Cybernetics & Systemics Journalen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorIslam, Almasrien
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