The use of contextual spatial knowledge for low-quality image segmentation

In this paper, a novel possibilistic approach for representing pixelic spatial knowledge is proposed to be used in classification; more specifically in segmentation of low quality images. This approach uses the spatial contextual information at the pixel level in order to produce a local possibility distribution. The similarity between this local possibility distribution representing the contextual pixelic information and the possibility distribution for each of the predetermined thematic classes is measured. This measure is used to assign one of these thematic classes to the pixel. In order to show the potential of the proposed possibilistic approach, synthetic and real images (Melanoma) are classified using the possibilistic similarity. The performance is compared with four relevant classic methods and one recent theory-like method (fuzzy c means). Our context-based possibilistic representation approach outperforms the other methods, in terms of classification recognition rate as well as in stability or robustness behavior when compared to those methods.