Color and Texture Based Segmentation Algorithm for Multicolor Textured Images

Abstract

We propose a color-texture image segmentation algorithm based on multistep region growing. This algorithm is able to deal with multicolored textures. Each of the colors in the texture to be segmented is considered as reference color. In this algorithm color and texture information are extracted from the image by the construction of color distances images, one for each reference color, and a texture energy image. The color distance images are formed by calculating CIEDE2000 distance in the L * a * b * color space to the colors that compound the multicolored texture. The texture energy image is extracted from some statistical moments. The method segment the color information by means of an adaptative N-dimensional region growing where N is the number of reference colors. The tolerance parameter is increased iteratively until an optimum is found and its growth is determined by a step size which depends on the variance on each distance image for the actual grown region. The criterium to decide which is the optimum value of the tolerance parameter depends on the contrast along the edge of the region grown, choosing the one which provides the region with the highest mean contrast in relation to the background. Additionally, this color multistep region growing is texture-controlled, in the sense that an extra condition to include a particular pixel in a region is demanded: the pixel needs to have the same texture as the rest of the pixels within the region. Results prove that the proposed method works very well with general purpose images and significantly improves the results obtained with other previously published algorithm (Fondón et al, 2006).