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Exploiting monge structures in optimum subwindow search

An, Senjian, Peursum, Patrick, Liu, Wanquan, Venkatesh, Svetha and Chen, Xiaoming 2010, Exploiting monge structures in optimum subwindow search, in CVPR 2010 :Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, [San Francisco, Calif.], pp. 926-933, doi: 10.1109/CVPR.2010.5540119.

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Title Exploiting monge structures in optimum subwindow search
Author(s) An, Senjian
Peursum, Patrick
Liu, Wanquan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Chen, Xiaoming
Conference name IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010 : San Francisco, Calif.)
Conference location San Francisco, Calif.
Conference dates 13-18 Jun. 2010
Title of proceedings CVPR 2010 :Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2010
Conference series Computer Society Conference on Computer Vision and Pattern Recognition
Start page 926
End page 933
Total pages 8
Publisher IEEE
Place of publication [San Francisco, Calif.]
Keyword(s) electronic switching systems
feature extraction
histograms
object detection
performance loss
search methods
search problems
shape
upper bound
Summary Optimum subwindow search for object detection aims to find a subwindow so that the contained subimage is most similar to the query object. This problem can be formulated as a four dimensional (4D) maximum entry search problem wherein each entry corresponds to the quality score of the subimage contained in a subwindow. For n x n images, a naive exhaustive search requires O(n4) sequential computations of the quality scores for all subwindows. To reduce the time complexity, we prove that, for some typical similarity functions like Euclidian metric, χ2 metric on image histograms, the associated 4D array carries some Monge structures and we utilise these properties to speed up the optimum subwindow search and the time complexity is reduced to O(n3). Furthermore, we propose a locally optimal alternating column and row search method with typical quadratic time complexity O(n2). Experiments on PASCAL VOC 2006 demonstrate that the alternating method is significantly faster than the well known efficient subwindow search (ESS) method whilst the performance loss due to local maxima problem is negligible.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9781424469840
ISSN 1063-6919
Language eng
DOI 10.1109/CVPR.2010.5540119
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044610

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.