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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tat-Jun Chin Suter, D. Hanzi Wang |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Computer Science, The University of Adelaide, South Australia (Tat-Jun Chin; Suter, D.; Hanzi Wang) |
| Abstract | Our goal is to fit the multiple instances (or structures) of a generic model existing in data. Here we propose a novel model selection scheme to estimate the number of genuine structures present. In contrast to conventional model selection approaches, our method is driven by kernel-based learning. The input data is first clustered based on their potential to have emerged from the same structure. However the number of clusters is deliberately overestimated to obtain a set of initial model fits onto the data. We then resolve the oversegmentation via a series of kernel optimisation conducted through multiple kernel learning, and the concept of kernel-target alignment is used as a model selection criterion. Experiments on synthetic and real data show that our method outperforms previous model selection schemes. We also focus on the application of multi-body motion segmentation. In particular we demonstrate success on estimating the number of motions on sequences with more than 3 unique motions. |
| Starting Page | 3586 |
| Ending Page | 3593 |
| File Size | 864280 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539931 |
| 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 | Kernel Computer vision Motion segmentation Motion estimation Motion detection Transmission line matrix methods Computer science Australia Solid modeling Cameras |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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