dc.contributor.author | Zhang, Huigang | |
dc.contributor.author | Bai, Xiao | |
dc.contributor.author | Cheng, Jian | |
dc.contributor.author | Zhou, Jun | |
dc.contributor.author | Zhao, Huijie | |
dc.contributor.editor | Gimelfarb, G | |
dc.contributor.editor | Hancock, E | |
dc.contributor.editor | Imiya, A | |
dc.contributor.editor | Kuijper, A | |
dc.contributor.editor | Kudo, M | |
dc.contributor.editor | Omachi, S | |
dc.contributor.editor | Windeatt, T | |
dc.contributor.editor | Yamada, K | |
dc.date.accessioned | 2018-03-27T01:30:51Z | |
dc.date.available | 2018-03-27T01:30:51Z | |
dc.date.issued | 2012 | |
dc.date.modified | 2013-01-04T00:19:48Z | |
dc.identifier.isbn | 9783642341656 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.refuri | http://www.media.imit.chiba-u.jp/ssspr2012/ | |
dc.identifier.doi | 10.1007/978-3-642-34166-3_53 | |
dc.identifier.uri | http://hdl.handle.net/10072/48881 | |
dc.description.abstract | The 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.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.language | English | |
dc.publisher | Springer | |
dc.publisher.place | United Kingdom | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition (SSPR) / International Workshop on Statistical Techniques in Pattern Recognition (SPR) | |
dc.relation.ispartofconferencetitle | STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION | |
dc.relation.ispartofdatefrom | 2012-11-07 | |
dc.relation.ispartofdateto | 2012-11-09 | |
dc.relation.ispartoflocation | Hokkaido Univ, Hiroshima, JAPAN | |
dc.relation.ispartofpagefrom | 483 | |
dc.relation.ispartofpageto | 491 | |
dc.relation.ispartofvolume | 7626 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Computer vision | |
dc.subject.fieldofresearchcode | 460304 | |
dc.title | An Incremental Structured Part Model for Image Classification | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
dc.description.version | Accepted Manuscript (AM) | |
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.issued | 2012 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Zhou, Jun | |