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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Senjian An Peursum, P. Wanquan Liu Venkatesh, S. Xiaoming Chen |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept. of Computing, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia (Senjian An; Peursum, P.; Wanquan Liu; Venkatesh, S.; Xiaoming Chen) |
| Abstract | 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 × n images, a naive exhaustive search requires $O(n^{4})$ 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, $χ^{2}$ 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 $O(n^{3}).$ Furthermore, we propose a locally optimal alternating column and row search method with typical quadratic time complexity $O(n^{2}).$ 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. |
| Starting Page | 926 |
| Ending Page | 933 |
| File Size | 162595 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540119 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electronic switching systems Search problems Object detection Histograms Feature extraction Shape Australia Search methods Performance loss Upper bound |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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