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dc.contributor.authorZhou, H
dc.contributor.authorBai, X
dc.contributor.authorZhou, J
dc.contributor.authorYang, H
dc.contributor.authorLiu, Y
dc.contributor.editorCheng-Lin Liu, Bin Luo, Walter G. Kropatsch, Jian Cheng
dc.date.accessioned2017-10-20T03:01:41Z
dc.date.available2017-10-20T03:01:41Z
dc.date.issued2015
dc.identifier.isbn9783319182230
dc.identifier.issn0302-9743
dc.identifier.refurihttp://www.nlpr.ia.ac.cn/gbr2015/
dc.identifier.doi10.1007/978-3-319-18224-7_16
dc.identifier.urihttp://hdl.handle.net/10072/69691
dc.description.abstractHyperspectral imagery has been widely used in real applications such as remote sensing, agriculture, surveillance, and geological analysis. Matching hyperspectral images is a challenge task due to the high dimensional nature of the data. The matching task becomes more difficult when images with different dimensions, such as a hyperspectral image and an RGB image, have to be matched. In this paper, we address this problem by investigating structured support vector machine to learn graph model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using graph matching algorithm on weighted graph models. We validate the effectiveness of our method through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherSpringer International Publishing
dc.publisher.placeSwitzerland
dc.publisher.urihttp://www.nlpr.ia.ac.cn/gbr2015/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameGbR2015
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2015-05-13
dc.relation.ispartofdateto2015-05-15
dc.relation.ispartoflocationBeijing, China
dc.relation.ispartofpagefrom158
dc.relation.ispartofpageto167
dc.relation.ispartofvolume9069
dc.rights.retentionY
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchcode080104
dc.titleLearning Graph Model for Different Dimensions Image Matching
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2015 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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

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