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Conference Paper: Mode seeking with an adaptive distance measure

TitleMode seeking with an adaptive distance measure
Authors
KeywordsMean Shift Algorithm
Metric Learning
Adaptive distance measure
Adaptive mode
Clustering accuracy
Issue Date2012
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 3rd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2012) held in conjunction with the 12th European Conference on Computer Vision (ECCV 2012), Florence, Italy, 7-13 October 2012. In Lecture Notes in Computer Science, 2012, v. 7585, p. 213-222 How to Cite?
AbstractThe mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy. © 2012 Springer-Verlag.
DescriptionLNCS v. 7585 has title: Computer Vision – ECCV 2012. Workshops and Demonstrations (Pt. 3)
Persistent Identifierhttp://hdl.handle.net/10722/164929
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorPan, Gen_US
dc.contributor.authorShang, Len_US
dc.contributor.authorSchnieders, Den_US
dc.contributor.authorWong, KKYen_US
dc.date.accessioned2012-09-20T08:12:27Z-
dc.date.available2012-09-20T08:12:27Z-
dc.date.issued2012en_US
dc.identifier.citationThe 3rd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (ARTEMIS 2012) held in conjunction with the 12th European Conference on Computer Vision (ECCV 2012), Florence, Italy, 7-13 October 2012. In Lecture Notes in Computer Science, 2012, v. 7585, p. 213-222en_US
dc.identifier.isbn978-3-642-33884-7-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/164929-
dc.descriptionLNCS v. 7585 has title: Computer Vision – ECCV 2012. Workshops and Demonstrations (Pt. 3)-
dc.description.abstractThe mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy. © 2012 Springer-Verlag.-
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectMean Shift Algorithm-
dc.subjectMetric Learning-
dc.subjectAdaptive distance measure-
dc.subjectAdaptive mode-
dc.subjectClustering accuracy-
dc.titleMode seeking with an adaptive distance measureen_US
dc.typeConference_Paperen_US
dc.identifier.emailPan, G: gdpan@hku.hken_US
dc.identifier.emailShang, L: lfshang@cs.hku.hken_US
dc.identifier.emailSchnieders, D: sdirk@cs.hku.hk-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393en_US
dc.identifier.doi10.1007/978-3-642-33885-4_22-
dc.identifier.scopuseid_2-s2.0-84867713932-
dc.identifier.hkuros210069en_US
dc.identifier.volume7585-
dc.identifier.spage213-
dc.identifier.epage222-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 130508-

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