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dc.contributor.authorAlaei, Fahimeh
dc.contributor.authorAlaei, Alireza
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.contributor.editorLiew, AWC
dc.contributor.editorLovell, B
dc.contributor.editorFookes, C
dc.contributor.editorZhou, J
dc.contributor.editorGao, Y
dc.contributor.editorBlumenstein, M
dc.contributor.editorWang, Z
dc.date.accessioned2017-06-07T00:12:12Z
dc.date.available2017-06-07T00:12:12Z
dc.date.issued2016
dc.identifier.doi10.1109/DICTA.2016.7797033
dc.identifier.urihttp://hdl.handle.net/10072/339092
dc.description.abstractThe tendency of current technology is towards a paperless world. Due to the rapid increase of digitized documents, providing a fast and easy method for retrieval is in high demand. The aim of this paper is to examine the effectiveness of texture features for document image retrieval. Thus, segmentation-free document image retrieval using a binary texture method is proposed. In the proposed approach, local features are extracted, local grey-level structures are summarised, and their distribution is characterised using global features. The assumption is that texture properties in the text regions and non-text regions of the document images are different. This assumption is used to rank the available document images and retrieve only those, which have greatest visual similarity to a given query. The under-sampled image and sub-images of the original image are further considered to improve the retrieval results, which are up to 76.0% in the first ranking and 96.2% in the Top-10 ranking. The Media Team Oulu Document Database, which is a heterogeneous database that offers a great variety of page layouts and contents, is used for experimentation.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeAustralia
dc.relation.ispartofconferencenameInternational Conference on Digital Image Computing - Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
dc.relation.ispartofdatefrom2016-11-30
dc.relation.ispartofdateto2016-12-02
dc.relation.ispartoflocationGold Coast, AUSTRALIA
dc.relation.ispartofpagefrom456
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto462
dc.relation.ispartofpageto7 pages
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleDocument Image Retrieval Based on Texture Features: A Recognition-Free Approach
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© 2016 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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