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dc.contributor.authorZhang, Huigang
dc.contributor.authorBai, Xiao
dc.contributor.authorCheng, Jian
dc.contributor.authorZhou, Jun
dc.contributor.authorZhao, Huijie
dc.contributor.editorGimelfarb, G
dc.contributor.editorHancock, E
dc.contributor.editorImiya, A
dc.contributor.editorKuijper, A
dc.contributor.editorKudo, M
dc.contributor.editorOmachi, S
dc.contributor.editorWindeatt, T
dc.contributor.editorYamada, K
dc.date.accessioned2018-03-27T01:30:51Z
dc.date.available2018-03-27T01:30:51Z
dc.date.issued2012
dc.date.modified2013-01-04T00:19:48Z
dc.identifier.isbn9783642341656
dc.identifier.issn0302-9743
dc.identifier.refurihttp://www.media.imit.chiba-u.jp/ssspr2012/
dc.identifier.doi10.1007/978-3-642-34166-3_53
dc.identifier.urihttp://hdl.handle.net/10072/48881
dc.description.abstractThe state-of-the-art image classification methods usually require many training samples to achieve good performance. To tackle this problem, we present a novel incremental method in this paper, which learns a part model to classify objects using only a small number of training samples. Our model captures the inherent connections of the semantic parts of objects and builds structural relationship between them. In the incremental learning stage, we use high entropy images that have been accepted by users to update the learned model. The proposed approach is evaluated on two datasets, which demonstrates its advantages over several alternative classification methods in the literature.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameJoint IAPR International Workshop on Structural and Syntactic Pattern Recognition (SSPR) / International Workshop on Statistical Techniques in Pattern Recognition (SPR)
dc.relation.ispartofconferencetitleSTRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION
dc.relation.ispartofdatefrom2012-11-07
dc.relation.ispartofdateto2012-11-09
dc.relation.ispartoflocationHokkaido Univ, Hiroshima, JAPAN
dc.relation.ispartofpagefrom483
dc.relation.ispartofpageto491
dc.relation.ispartofvolume7626
dc.rights.retentionY
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode080109
dc.titleAn Incremental Structured Part Model for Image Classification
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.rights.copyright© 2012 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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