Graphical Models and Image Processing
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
A survey of kernel and spectral methods for clustering
Pattern Recognition
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level set image segmentation with Bayesian analysis
Neurocomputing
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Fuzzy c-means clustering with non local spatial information for noisy image segmentation
Frontiers of Computer Science in China
IEEE Transactions on Image Processing
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Robust RML estimator - fuzzy c-means clustering algorithms for noisy image segmentation
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Fuzzy spectral clustering with robust spatial information for image segmentation
Applied Soft Computing
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
As one of widely used clustering algorithms, spectral clustering clusters data using the eigenvectors of the Laplacian matrix derived from a dataset and has been successfully applied to image segmentation. However, spectral clustering algorithms are sensitive to noise and other imaging artifacts because of not taking into account the spatial information of the pixels in the image. In this paper, a novel non-local spatial spectral clustering algorithm for image segmentation is presented. In the proposed method, the objective function of weighted kernel k-means algorithm is firstly modified by incorporating the non-local spatial constraint term. Then the equivalence between the objective functions of normalized cut and weighted kernel k-means with non-local spatial constraints is given and a novel non-local spatial matrix is constructed to replace the normalized Laplacian matrix. Finally, spectral clustering techniques are applied to this matrix to obtain the final segmentation result. The novel algorithm is performed on synthetic and real images, especially magnetic resonance (MR) images, and compared with the traditional spectral clustering algorithms and segmentation algorithms with spatial information. Experimental results demonstrate that the proposed algorithm is robust to noise in the image and obtains more effective performance than the comparison algorithms.