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Article: Merging toward natural clusters

TitleMerging toward natural clusters
Authors
KeywordsBoundary Detection
Distinctness Predicate
Image Segmentation
Region Merging
Issue Date2009
PublisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/oe
Citation
Optical Engineering, 2009, v. 48 n. 7, article no. 077202 How to Cite?
AbstractTo findout how many clusters exist in a sample set is an old yet unsolved problem in unsupervised clustering. This problem inevitably occurs in region merging/growing, a well studied and popular technique in image segmentation. Region merging usually needs a stop criterion. The stop criterion is not automatically determined and often has to be set manually to arrive at a sensible segmentation, which is rather difficult for natural images. To address this problem, we present a robust stop criterion that is based on a novel distinctness predicate for adjacent regions. The predicate discerns distinct regions by examining the evidence of the boundary between neighboring regions. Requiring that every region should be distinct from each other, the proposed method is able to choose a stop point where a natural partition is most likely. Under a region merging framework, we demonstrate the effectiveness of the stop criterion using two merging criterion: one based on optimizing a global functional, and another based on a local criterion. Experimental results and comparison are given at the end. © 2009 Society of Photo-Optical Instrumentation Engineers.
Persistent Identifierhttp://hdl.handle.net/10722/155694
ISSN
2015 Impact Factor: 0.984
2015 SCImago Journal Rankings: 0.485
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTan, ZGen_US
dc.contributor.authorYung, NHCen_US
dc.date.accessioned2012-08-08T08:34:51Z-
dc.date.available2012-08-08T08:34:51Z-
dc.date.issued2009en_US
dc.identifier.citationOptical Engineering, 2009, v. 48 n. 7, article no. 077202en_US
dc.identifier.issn0091-3286en_US
dc.identifier.urihttp://hdl.handle.net/10722/155694-
dc.description.abstractTo findout how many clusters exist in a sample set is an old yet unsolved problem in unsupervised clustering. This problem inevitably occurs in region merging/growing, a well studied and popular technique in image segmentation. Region merging usually needs a stop criterion. The stop criterion is not automatically determined and often has to be set manually to arrive at a sensible segmentation, which is rather difficult for natural images. To address this problem, we present a robust stop criterion that is based on a novel distinctness predicate for adjacent regions. The predicate discerns distinct regions by examining the evidence of the boundary between neighboring regions. Requiring that every region should be distinct from each other, the proposed method is able to choose a stop point where a natural partition is most likely. Under a region merging framework, we demonstrate the effectiveness of the stop criterion using two merging criterion: one based on optimizing a global functional, and another based on a local criterion. Experimental results and comparison are given at the end. © 2009 Society of Photo-Optical Instrumentation Engineers.en_US
dc.languageengen_US
dc.publisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://www.spie.org/oeen_US
dc.relation.ispartofOptical Engineeringen_US
dc.rightsOptical Engineering. Copyright © SPIE - International Society for Optical Engineering-
dc.rightsCopyright 2009 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.subjectBoundary Detectionen_US
dc.subjectDistinctness Predicateen_US
dc.subjectImage Segmentationen_US
dc.subjectRegion Mergingen_US
dc.titleMerging toward natural clustersen_US
dc.typeArticleen_US
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_US
dc.identifier.authorityYung, NHC=rp00226en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1117/1.3183892en_US
dc.identifier.scopuseid_2-s2.0-81055143894en_US
dc.identifier.hkuros164693-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-81055143894&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume48en_US
dc.identifier.issue7en_US
dc.identifier.isiWOS:000268489400038-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridTan, ZG=26427814600en_US
dc.identifier.scopusauthoridYung, NHC=7003473369en_US

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