Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fuzzy Sets and Systems
ACM Computing Surveys (CSUR)
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
Partially supervised clustering for image segmentation
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Histogram-based segmentation in a perceptually uniform color space
IEEE Transactions on Image Processing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Perceptually uniform color spaces for color texture analysis: an empirical evaluation
IEEE Transactions on Image Processing
A framework with modified fast FCM for brain MR images segmentation
Pattern Recognition
Fuzzy c-means clustering with weighted image patch for image segmentation
Applied Soft Computing
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A fuzzy c-means algorithm incorporating the notion of dominant colors and spatial homogeneity is proposed for the color clustering problem. The proposed algorithm extracts the most vivid and distinguishable colors, referred to as the dominant colors, and then used these colors as the initial centroids in the clustering calculations. This is achieved by introducing reference colors and defining a fuzzy membership model between a color point and each reference color. The objective function of the proposed algorithm incorporates the spatial homogeneity, which reflects the uniformity of a region. The homogeneity is quantified in terms of the variance and discontinuity of the spatial neighborhood around a color point. The effectiveness and reliability of the proposed method is demonstrated through various color clustering examples.