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Conference Paper: Retrieving biomedical images through content-based learning from examples using fine granularity

TitleRetrieving biomedical images through content-based learning from examples using fine granularity
Authors
Issue Date2012
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml?WT.svl=mddp2
Citation
Conference 8319 - Medical Imaging 2012: Advanced PACS-based Imaging Informatics and Therapeutic Applications, San Diego, California, USA, 4 February 2012. In Proceedings of SPIE, 2012, v. 8319, p. article no. 83190M How to Cite?
AbstractTraditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.
DescriptionSession: Data Mining II
Persistent Identifierhttp://hdl.handle.net/10722/169311
ISSN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_US
dc.contributor.authorXu, Sen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2012-10-18T08:49:50Z-
dc.date.available2012-10-18T08:49:50Z-
dc.date.issued2012en_US
dc.identifier.citationConference 8319 - Medical Imaging 2012: Advanced PACS-based Imaging Informatics and Therapeutic Applications, San Diego, California, USA, 4 February 2012. In Proceedings of SPIE, 2012, v. 8319, p. article no. 83190Men_US
dc.identifier.issn0277-786X-
dc.identifier.urihttp://hdl.handle.net/10722/169311-
dc.descriptionSession: Data Mining II-
dc.description.abstractTraditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.-
dc.languageengen_US
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml?WT.svl=mddp2-
dc.relation.ispartofProceedings of SPIE - International Society for Optical Engineeringen_US
dc.rightsProceedings of SPIE - International Society for Optical Engineering. Copyright © S P I E - International Society for Optical Engineering.-
dc.rightsCopyright notice format: Copyright 2012 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleRetrieving biomedical images through content-based learning from examples using fine granularityen_US
dc.typeConference_Paperen_US
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hken_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1117/12.913765-
dc.identifier.hkuros211543en_US
dc.identifier.volume8319-
dc.identifier.spagearticle no. 83190M-
dc.identifier.epagearticle no. 83190M-
dc.identifier.isiWOS:000304871200016-
dc.publisher.placeUnited States-

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