Robust Multi-Body Motion Segmentation Based on Fuzzy k-Subspace Clustering

Xi LI  Zhengnan NING  Liuwei XIANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.11    pp.2609-2614
Publication Date: 2005/11/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.11.2609
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
motion segmentation,  fuzzy,  k-subspace clustering,  weighted SVD,  

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The problem of multi-body motion segmentation is important in many computer vision applications. In this paper, we propose a novel algorithm called fuzzy k-subspace clustering for robust segmentation. The proposed method exploits the property that under orthographic camera model the tracked feature points of moving objects reside in multiple subspaces. We compute a partition of feature points into corresponding subspace clusters. First, we find a "soft partition" of feature points based on fuzzy k-subspace algorithm. The proposed fuzzy k-subspace algorithm iteratively minimizes the objective function using Weighted Singular Value Decomposition. Then the points with high partition confidence are gathered to form the subspace bases and the remaining points are classified using their distance to the bases. The proposed method can handle the case of missing data naturally, meaning that the feature points do not have to be visible throughout the sequence. The method is robust to noise and insensitive to initialization. Extensive experiments on synthetic and real data show the effectiveness of the proposed fuzzy k-subspace clustering algorithm.