Using opponent correlation functions to recognize color texture

We propose a new approach to model texture in 2D color images based on opponent correlation functions computed within and between sensor bands of a red-green-blue image. The new opponent correlations cover all the possible opponent autocorrelations and opponent crosscorrelations of a color image. Invariants of opponent correlations are computed with the aid of Zernike moments to preserve rotation, translation, and scale invariance. The method can be used in the classification (even with slight illumination changes) and the unsupervised segmentation of color texture in a variety of image processing and pattern recognition tasks. One of the most important points in introducing the opponent correlation functions is the efficient computation compared to the direct spatial correlation method that requires heavy computations.