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dc.contributor.authorYou, Jia
dc.contributor.authorShen, Hong
dc.contributor.authorCohen, H.
dc.date.accessioned2019-12-04T05:30:13Z
dc.date.available2019-12-04T05:30:13Z
dc.date.issued1997
dc.identifier.issn1045926X
dc.identifier.doi10.1006/jvlc.1997.0044
dc.identifier.urihttp://hdl.handle.net/10072/121206
dc.description.abstractThis paper proposes an efficient parallel approach to texture classification for image retrieval. The idea behind this method is to pre-extract texture features in terms of texture energy measurement associated with a ‘tuned’ mask and store them in a multiscale and multi-orientation texture class database via a two-dimensional linked list for query. Thus, each texture class sample in the database can be traced by its texture energy in a two-dimensional row-sorted matrix. The parallel searching strategies are introduced for fast identification of the entities closest to the input texture throughout the given texture energy matrix. In contrast to the traditional search methods, our approach incorporates different computation patterns for different cases of available processor numbers and concerns with robust and work-optimal parallel algorithms for row-search and minimum-find based on the accelerated cascading technique and the dynamic processor allocation scheme. Applications of the proposed parallel search and multisearch algorithms to both single image classification and multiple image classification are discussed. The time complexity analysis shows that our proposal will speed up the classification tasks in a simple but dynamic manner. Examples of the texture classification task applied to image retrieval of Brodatz textures, comprising various orientations and scales are presented.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAcademic Press
dc.publisher.placeUK
dc.relation.ispartofpagefrom359
dc.relation.ispartofpageto372
dc.relation.ispartofissue3
dc.relation.ispartofjournalJournal of Visual Languages and Computing
dc.relation.ispartofvolume8
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchComputer Software
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0803
dc.subject.fieldofresearchcode1702
dc.titleAn efficient parallel texture classification for image retrieval
dc.typeJournal article
dc.type.descriptionC1 - Peer Reviewed (HERDC)
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorYou, Jia
gro.griffith.authorShen, Hong


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