Spectral clustering with fuzzy similarity measure |
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Authors: | Feng Zhao Hanqiang Liu Licheng Jiao[Author vitae] |
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Affiliation: | aSchool of Telecommunications and Information Engineering, Xi?an University of Posts and Telecommunications, Xi?an, PR China;bSchool of computer science, Shannxi Normal University, Xi?an, PR China;cKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi?an, PR China |
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Abstract: | Spectral clustering algorithms have been successfully used in the field of pattern recognition and computer vision. The widely used similarity measure for spectral clustering is Gaussian kernel function which measures the similarity between data points. However, it is difficult for spectral clustering to choose the suitable scaling parameter in Gaussian kernel similarity measure. In this paper, utilizing the prototypes and partition matrix obtained by fuzzy c-means clustering algorithm, we develop a fuzzy similarity measure for spectral clustering (FSSC). Furthermore, we introduce the K-nearest neighbor sparse strategy into FSSC and apply the sparse FSSC to texture image segmentation. In our experiments, we firstly perform some experiments on artificial data to verify the efficiency of the proposed fuzzy similarity measure. Then we analyze the parameters sensitivity of our method. Finally, we take self-tuning spectral clustering and Nyström methods for baseline comparisons, and apply these three methods to the synthetic texture and remote sensing image segmentation. The experimental results show that the proposed method is significantly effective and stable. |
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Keywords: | Spectral clustering Fuzzy clustering Similarity measure Texture feature Image segmentation Remote sensing image |
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