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Color image segmentation using a possibilistic approach

In this paper, we used the possibilistic approach (PCM) for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. So, the problems in the fuzzy c-mean (FCM) can be solved by the PCM algorithm. Our experiments by using the PCM algorithm only were tested on different color spaces. The clustering results by the PCM were not smoothly bounded, and they often had holes. The region growing was used as a postprocessing after smoothing the noise points in pixel seeds. In our experiments, we illustrate that the PCM algorithm performs reasonably well compared with the FCM algorithm in noisy environments.