Painting algorithms for fuzzy classification

Abstract

Land cover analysis by means of remotely sensing images quite often suggest the existence of fuzzy classes, where no clear borders or particular shapes appear. In this paper we present an image classification aid algorithm which shows as its main output a processed image where each pixel is being colored according to the degree of similitude to their respective surrounding pixels. Such a processed image is therefore suggesting possible classes, to be implemented in a more sophisticated image classification process. A key underlying argument for this approach is the relevance of painting techniques in order to help decision makers to understand complex information relative to fuzzy image classification.

Unsupervised Perceptual Model for Color Image's Segmentation

“…The coloring algorithm for fuzzy graphs proposed in [2] is a succession of binary coloring for crisp graphs, defined by the set of fuizzy edges that are lower than a prescribed threshold. The first binary coloring analyzes the pixels set P classifying each pixel as 0 or 1.…”

“…In this way, a c-coloring (see [2] and [4]) will be defined on G(I), showing possible homogeneous regions in the image.…”