Improved subspace clustering via exploitation of spatial constraints Pham, Duc-Son, Saha, Budhaditya, Phung, Dinh and Venkatesh, Svetha 2012, Improved subspace clustering via exploitation of spatial constraints, in CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, N. J., pp. 550-557. Attached Files Name Description MIMEType Size Downloads Title Improved subspace clustering via exploitation of spatial constraints Author(s) Pham, Duc-Son Saha, Budhaditya orcid.org/0000-0001-8011-6801 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Computer Vision and Pattern Recognition. Conference (2012 : Providence, R. I.) Conference location Providence, R. I. Conference dates 16-21 Jun. 2012 Title of proceedings CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Editor(s) [Unknown] Publication date 2012 Conference series Computer Vision and Pattern Recognition. Conference (2012 : Providence, R. I.) Start page 550 End page 557 Total pages 8 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) computational costs corrupted data data sets Hopkins motion segmentation numerical algorithms sparse representation sparse solutions spatial constraints spatial geometry subspace clustering Summary We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation. ISBN 1467312266 9781467312264 ISSN 1063-6919 Language eng Field of Research 080104 Computer Vision Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30049552 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 29 Nov 2012, 07:58:12 EST Wed, 05 Dec 2012, 08:59:18 EST Wed, 05 Dec 2012, 09:00:26 EST Tue, 07 May 2013, 17:04:39 EST Wed, 08 May 2013, 08:35:10 EST Mon, 27 May 2013, 13:55:23 EST Tue, 24 Jun 2014, 11:42:23 EST Fri, 08 Aug 2014, 10:19:25 EST Sat, 15 Apr 2017, 02:35:08 EST Filtered Full Citation counts:   Cited 14 times in TR Web of Science Cited 19 times in Scopus Search Google Scholar Access Statistics: 472 Abstract Views, 14 File Downloads  -  Detailed Statistics Created: Thu, 29 Nov 2012, 07:58:12 EST

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