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Conference Paper: Retrieving biomedical images through content-based learning from examples using fine granularity
Title | Retrieving biomedical images through content-based learning from examples using fine granularity |
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Authors | |
Keywords | Biomedical image search Content-based image retrieval Learning from online examples |
Issue Date | 2012 |
Publisher | S 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? |
Abstract | Traditional 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. |
Description | Session: Data Mining II |
Persistent Identifier | http://hdl.handle.net/10722/169311 |
ISSN | 2020 SCImago Journal Rankings: 0.192 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Jiang, H | en_US |
dc.contributor.author | Xu, S | en_US |
dc.contributor.author | Lau, FCM | en_US |
dc.date.accessioned | 2012-10-18T08:49:50Z | - |
dc.date.available | 2012-10-18T08:49:50Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.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 | en_US |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/10722/169311 | - |
dc.description | Session: Data Mining II | - |
dc.description.abstract | Traditional 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.language | eng | en_US |
dc.publisher | S 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.ispartof | Proceedings of SPIE - International Society for Optical Engineering | en_US |
dc.rights | Copyright 2012 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. This article is available online at https://doi.org/10.1117/12.913765 | - |
dc.subject | Biomedical image search | - |
dc.subject | Content-based image retrieval | - |
dc.subject | Learning from online examples | - |
dc.title | Retrieving biomedical images through content-based learning from examples using fine granularity | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Lau, FCM: fcmlau@cs.hku.hk | en_US |
dc.identifier.authority | Lau, FCM=rp00221 | en_US |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1117/12.913765 | - |
dc.identifier.scopus | eid_2-s2.0-84861879627 | - |
dc.identifier.hkuros | 211543 | en_US |
dc.identifier.volume | 8319 | - |
dc.identifier.spage | article no. 83190M | - |
dc.identifier.epage | article no. 83190M | - |
dc.identifier.isi | WOS:000304871200016 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0277-786X | - |