Spectral clustering steered low-rank representation for subspace segmentation |
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| Affiliation: | 1. Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan;2. Institute of Computer Science and Technology, Peking University, Beijing, China;3. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan;1. Dept. of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan;2. Dept. of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan;3. Dept. of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan;4. Institute of Information Science, Academia Sinica, Taipei, Taiwan;1. University of the Basque Country UPV/EHU, Manuel Lardizabal, 1, 20018 San Sebastian, Spain;2. IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain;3. Doctoral School of Sciences and Technology, Lebanese University, Mitein Street, Tripoli, Lebanon;1. College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, PR China;2. National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, PR China;1. Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, PR China;2. School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia |
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| Abstract: | Low-rank representation (LRR) and its variations have achieved great successes in subspace segmentation tasks. However, the segmentation processes of the existing LRR-related methods are all divided into two separated steps: affinity graphs construction and segmentation results obtainment. In the second step, normalize cut (Ncut) algorithm is used to get the final results based on the constructed graphs. This implies that the affinity graphs obtained by LRR-related algorithms may not be most suitable for Ncut, and the best results are not guaranteed to be achieved. In this paper, we propose a spectral clustering steered LRR representation algorithm (SCSLRR) which combines the objection functions of Ncut, K-means and LRR together. By solving a joint optimization problem, SCSLRR is able to find low-rank affinity matrices which are most beneficial for Ncut to get best segmentation results. The extensive experiments of subspace segmentation on several benchmark datasets show that SCSLRR dominates the related methods. |
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| Keywords: | Subspace segmentation Sparse representation low-rank representation Normalize cut |
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