Multi-structure model selection via kernel optimisation

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 multibody motion segmentation. In particular we demonstrate success on estimating the number of motions on sequences with more than 3 unique mot...

• Type: Journal
• Year: 2010
• Where: CVPR
• Authors: Tat-Jun Chin, David Suter, Hanzi Wang
• Keywords: Computer Vision, Conventional Model Selection, CVPR 2010, Model Selection, Model Selection Schemes