AbstractTo study human image cognition is more than ever an important topic since the number of vision-based materials has been increased over the years. Texture seems to be a powerful tool to describe the appearances of objects. Therefore, very flexible and powerful texture descriptors are of importance that allow to recognize the texture and to understand what makes up the texture. The most used texture descriptor is the well-known texture descriptor based on the co-occurrence matrix. We propose a texture descriptor based on random sets. This descriptor gives us more freedom in describing different textures. In this paper, we compare the two texture descriptors based on a medical data set. We review the theory of the two texture descriptors and describe the procedure for the comparison of the two methods. Polyp images are used that are derived from colon examination. Decision tree induction is used to learn a classifier model. Cross-validation is used to calculate the error rate. The comparison of the two texture descriptors is based on the error rate, the properties of the two best classification models, the runtime for the feature calculation, the selected features, and the semantic meaning of the texture descriptors. The medical data set was chosen since texture seems to play an important role in describing medical objects.