원문정보
초록
영어
The efficiency of spectral clustering depends heavily on the similarity measure adopted. A widely used similarity measure is the Gaussian kernel function where Euclidean distance is used. Unfortunately, the result of spectral clustering is very sensitive to the scaling parameter and the Euclidean distance is usually not suitable to the complex distribution data. In this paper, a spectral clustering algorithm based on fuzzy partition similarity measure ( FPSC) is presented to solve the problem that result of spectral clustering is very sensitive to scaling parameter. The proposed algorithm is steady extremely and hardly affected by the scaling parameter. Experiments on three benchmark datasets, two synthetic texture images are made, and the results demonstrate the effectiveness of the proposed algorithm.
목차
1. Introduction
2. Spectral Clustering Algorithm and the NJW Method
3. The Proposed FPSC Method
3.1. Kernel Fuzzy C-Means Clustering (KFCM)
3.2. Kernel Fuzzy Similarity Measure
3.3. FPSC Method for Texture Image Segmentation
4. Experimental Results and Analysis
4.1. Experiments on Three Benchmark Synthetic Datasets
4.2. Sensitivity Analysis of Parameters
4.3. Experiments on two Textures Images
5. Conclusion
References