Multi-view feature extraction based on slow feature analysis

In this paper, we proposed to apply IncSFA to represent the feature of 3D model and employed graph matching to handle similarity measure problem between two different 3D model. First, we built the input data in order to guarantee it suitable for SFA mode according to structure information of 3D model. Second, SFA method utilizes iterations learning method to extract slow feature for each 2D views recorded from 3D model. Finally, weighted bipartite graph matching is leveraged to compute the similarity between query model and candidate model. Extensive comparison experiments were on the popular ETH dataset. The results demonstrate the superiority of the proposed method.

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