A color image segmentation approach based on fuzzy similarity measure

We propose an image segmentation scheme based on a fuzzy color similarity measure to segment out meaningful objects in an image according to human perception. The proposed method first defines a set of fuzzy colors based on the HLS color coordinate space. Each pixel in an image is represented by a set of fuzzy colors that are the most similar colors in the color palette selected by humans. Then, a fuzzy similarity measure is developed for evaluating the similarity of fuzzy colors between two pixels. We recursively merge adjacent pixels to form meaningful objects by the fuzzy similarity measure until there is no similar color between adjacent pixels. Experiments demonstrate that the proposed method can extract meaningful objects from images effectively