A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

[1]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  B. Mandelbrot,et al.  Fractional Brownian Motions, Fractional Noises and Applications , 1968 .

[3]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[4]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[5]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[6]  Charles A. Bouman,et al.  Multiple Resolution Segmentation of Textured Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  F. Parmiggiani,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[8]  R. Voss.  Characterization and Measurement of Random Fractals , 1986 .

[9]  James C. Bezdek,et al.  Efficient Implementation of the Fuzzy c-Means Clustering Algorithms , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Nirupam Sarkar,et al.  Improved fractal geometry based texture segmentation technique , 1993 .

[11]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[12]  Dimitar Filev,et al.  Book review: Essentials of Fuzzy Modeling and Control by Ronald R. Yager and Dimitar P. Filev (John Wiley, 1994) , 1994, SGAR.

[13]  Madasu Hanmandlu,et al.  Structure identification of generalized adaptive neuro-fuzzy inference systems , 2003, IEEE Trans. Fuzzy Syst..

[14]  R. Voss.  Random Fractals: characterization and measurement , 1991 .

[15]  Patrick C. Chen,et al.  Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm☆ , 1979 .