Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Color Discrimination with Classification Based on Feature Distributions
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Adaptive color segmentation-a comparison of neural and statistical methods
IEEE Transactions on Neural Networks
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This paper describes a clustering approach for color image segmentation using fuzzy classification principles. The method uses classification to group pixels into homogeneous regions. Both global and local information are taken into account. This is particularly helpful in taking care of small objects and local variation of color images. Color, mean and standard deviation are used as a data source. The classification is achieved by a new version of self-organizing maps algorithm . This new algorithm is equivalent to classic fuzzy C-mean algorithm (FCM) whose objective function has been modified. Code vectors that constitute centers of classes, are distributed on a regular low dimension grid. In addition, a penalization term is added to guarantee a smooth distribution of the values of the code vectors on the grid. Tests achieved on color images, followed by an automatic evaluation revealed the good performances of the proposed method.