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|Title:||Multi-structure model selection via kernel optimisation|
|Citation:||Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010; pp.3586-3593|
|Publisher:||IEEE COMPUTER SOC|
|Publisher Place:||10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA|
|Series/Report no.:||IEEE Conference on Computer Vision and Pattern Recognition|
|Conference Name:||IEEE Conference on Computer Vision and Pattern Recognition (23rd : 2010 : San Francisco, CA)|
|Tat-Jun Chin, David Suter and 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.|
|Appears in Collections:||Computer Science publications|
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