Show simple item record

dc.contributor.authorZhou, Jun
dc.contributor.authorFu, Zhouyu
dc.contributor.authorRobles-Kelly, Antonio
dc.contributor.editorShi, H
dc.contributor.editorZhang, YC
dc.contributor.editorBottema, MJ
dc.contributor.editorLovell, BC
dc.contributor.editorMaeder, AJ
dc.date.accessioned2017-05-03T16:11:47Z
dc.date.available2017-05-03T16:11:47Z
dc.date.issued2009
dc.date.modified2013-06-20T04:26:27Z
dc.identifier.isbn978-1-4244-5297-2
dc.identifier.doi10.1109/DICTA.2009.28
dc.identifier.urihttp://hdl.handle.net/10072/51724
dc.description.abstractIn this paper, we address the problem of recovering an optimal salient image descriptor transformation for image classification. Our method involves two steps. Firstly, a binary salient map is generated to specify the regions of interest for subsequent image feature extraction. To this end, an optimal cut-off value is recovered by maximising Fisher's linear discriminant separability measure so as to separate the salient regions from the background of the scene. Next, image descriptors are extracted in the foreground region in order to be optimally transformed. The descriptor optimisation problem is cast in a regularised risk minimisation setting, in which the aim of computation is to recover the optimal transformation up to a cost function. The cost function is convex and can be solved using quadratic programming. The results on unsegmented Oxford Flowers database show that the proposed method can achieve classification performance that are comparable to those provided by alternatives elsewhere in the literature which employ pre-segmented images.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent254188 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename11th Conference on Digital Image Computing: Techniques and Applications
dc.relation.ispartofconferencetitle2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2009)
dc.relation.ispartofdatefrom2009-12-01
dc.relation.ispartofdateto2009-12-03
dc.relation.ispartoflocationMelbourne, AUSTRALIA
dc.relation.ispartofpagefrom125
dc.relation.ispartofpageto+
dc.rights.retentionY
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchcode460304
dc.subject.fieldofresearchcode460306
dc.titleLearning the Optimal Transformation of Salient Features for Image Classification
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2009 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.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record