Show simple item record

dc.contributor.authorLiew, AWC
dc.contributor.authorYan, H
dc.contributor.authorLaw, NF
dc.date.accessioned2017-05-03T15:20:19Z
dc.date.available2017-05-03T15:20:19Z
dc.date.issued2005
dc.date.modified2009-03-26T06:42:29Z
dc.identifier.issn1063-6706
dc.identifier.doi10.1109/TFUZZ.2004.841748
dc.identifier.urihttp://hdl.handle.net/10072/21801
dc.description.abstractAn image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1439231 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.publisher.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=91
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom444
dc.relation.ispartofpageto453
dc.relation.ispartofissue4
dc.relation.ispartofjournalIEEE Transactions on Fuzzy Systems
dc.relation.ispartofvolume13
dc.rights.retentionY
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchcode4901
dc.titleImage Segmentation Based on Adaptive Cluster Prototype Estimation
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2005 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.
gro.date.issued2005
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record