In the problem of segmentation, the goal is to separate spatial regions of an image on the basis of similarity within each region and distinction between different regions. Approaches to color-based segmentation range from empirical evaluation of various color spaces [22], to clustering in feature space [26], to physics-based modeling [18]. The essential difference between color segmentation and color recognition is that the former uses color to separate objects without a priori knowledge about specific surfaces; the latter attempts to recognize colors of known color characteristics. Although the two problems are, in some sense, the inverse of each other, results from segmentation can be useful in recognition; for instance, Maxwell [18] shows the advantages of using normalized color and separating color from brightness.