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Conference Paper: Image segmentation towards natural clusters
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TitleImage segmentation towards natural clusters
 
AuthorsTan, Z1
Yung, NHC1
 
Issue Date2008
 
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545
 
CitationThe 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, FL., 8-11 December 2008. In International Conference on Pattern Recognition, 2008, p. 1-4 [How to Cite?]
 
AbstractTo find how many clusters in a sample set is an old yet unsolved problem in unsupervised clustering. Many segmentation methods require the user to specify the number of regions in the image or some delicate thresholds to get a sensible segmentation. In this paper, we propose a segmentation method that is able to automatically determine the number of regions in an image. The method effectively discerns distinct regions by analyzing the properties of the joint boundary between neighboring regions. By requiring that every region should be distinct from each other, it is able to choose a natural partition from the partition set which contains all possible partitions. Results are given at the end of this paper to demonstrate the effectiveness of this approach. © 2008 IEEE.
 
ISBN978-1-4244-2175-6
 
ISSN1051-4651
2012 SCImago Journal Rankings: 0.484
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorTan, Z
 
dc.contributor.authorYung, NHC
 
dc.date.accessioned2010-07-13T03:54:49Z
 
dc.date.available2010-07-13T03:54:49Z
 
dc.date.issued2008
 
dc.description.abstractTo find how many clusters in a sample set is an old yet unsolved problem in unsupervised clustering. Many segmentation methods require the user to specify the number of regions in the image or some delicate thresholds to get a sensible segmentation. In this paper, we propose a segmentation method that is able to automatically determine the number of regions in an image. The method effectively discerns distinct regions by analyzing the properties of the joint boundary between neighboring regions. By requiring that every region should be distinct from each other, it is able to choose a natural partition from the partition set which contains all possible partitions. Results are given at the end of this paper to demonstrate the effectiveness of this approach. © 2008 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationThe 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, FL., 8-11 December 2008. In International Conference on Pattern Recognition, 2008, p. 1-4 [How to Cite?]
 
dc.identifier.epage4
 
dc.identifier.hkuros164706
 
dc.identifier.isbn978-1-4244-2175-6
 
dc.identifier.issn1051-4651
2012 SCImago Journal Rankings: 0.484
 
dc.identifier.scopuseid_2-s2.0-77957934683
 
dc.identifier.spage1
 
dc.identifier.urihttp://hdl.handle.net/10722/62146
 
dc.languageeng
 
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545
 
dc.publisher.placeUnited States
 
dc.relation.ispartofInternational Conference on Pattern Recognition
 
dc.relation.referencesReferences in Scopus
 
dc.rightsInternational Conference on Pattern Recognition. Copyright © IEEE Computer Society.
 
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.titleImage segmentation towards natural clusters
 
dc.typeConference_Paper
 
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Author Affiliations
  1. The University of Hong Kong