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. ©2010 IEEE.
CITATION STYLE
Chin, T. J., Suter, D., & Wang, H. (2010). Multi-structure model selection via kernel optimisation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3586–3593). https://doi.org/10.1109/CVPR.2010.5539931
Mendeley helps you to discover research relevant for your work.