Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
A tutorial on spectral clustering
Statistics and Computing
A new segmentation system for brain MR images based on fuzzy techniques
Applied Soft Computing
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
Non-local spatial spectral clustering for image segmentation
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image Thresholding Using Graph Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Kernel generalized fuzzy c-means clustering with spatial information for image segmentation
Digital Signal Processing
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In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.