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dc.contributor.authorAlaei, Fahimeh
dc.contributor.authorAlaei, Alireza
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2019-06-08T01:41:09Z
dc.date.available2019-06-08T01:41:09Z
dc.date.issued2017
dc.identifier.isbn9781538642764
dc.identifier.issn2151-2191
dc.identifier.doi10.1109/IVCNZ.2017.8402464
dc.identifier.urihttp://hdl.handle.net/10072/380974
dc.description.abstractThe volume of digitised documents is increasing every day. Thus, designing a fast document image retrieval method for the large volume of document images, especially when the document images are also large in size, is of high demand. As feature extraction is one of the important steps in every document image retrieval system, a feature extraction technique with a low computing time and small feature number has a direct effect on the speed of the retrieval system. In this paper, we propose a non-parametric texture feature extraction method based on summarising the local grey-level structure of the image. To extract the proposed features, the input image is, at first, divided into a set of overlapping patches of equal size. The peripheral pixels of the centre pixel in a patch are used to extract two sets of patterns. The patterns are derived from the vertical & horizontal, and diagonal & off-diagonal pixels of the patch, separately. From each set of pixels, 15 different local binary patterns are extracted in our proposed feature extraction method. Two histograms of the local binary patterns are then created and concatenated to obtain 30 features called fast local binary pattern (F-LBP). To evaluate the efficiency of the proposed feature extraction method, MTDB and ITESOFT databases were considered for experimentation. The proposed F-LBP provided promising results with lower computing time as well as smaller memory space consumption compared to other variation of LBP methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofchapter43120
dc.relation.ispartofconferencenameInternational Conference on Image and Vision Computing New Zealand (IVCNZ)
dc.relation.ispartofconferencetitle2017 INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ)
dc.relation.ispartofdatefrom2017-12-04
dc.relation.ispartofdateto2017-12-06
dc.relation.ispartoflocationChristchurch, NEW ZEALAND
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleFast local binary pattern: Application to document image retrieval
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorAlaei, Ali Reza R.
gro.griffith.authorAlaei, Fahimeh


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