Periodicity, directionality, and randomness: Wold features for perceptual pattern recognition

One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as image retrieval, if is important that features used by the system in pattern comparison provide good measures of “perceptual similarity”. The authors present a new set of features and an image model based on the three mutually orthogonal components produced by the 2-D Wold decomposition of random fields. These components have visual properties which approximate the three most important perceptual dimensions of human texture perception. The method presented here is different from the existing Wold-based models in that it tolerates certain local inhomogeneities which arise in natural textures and reduces computation for comparison of patterns subjected to transformations such as rotation. An image retrieval algorithm based on the new texture model is presented. The effectiveness of the new Wold features for retrieving perceptually similar natural textures is demonstrated by comparing it to that of other well-known pattern recognition methods. The Wold model appears to offer a perceptually more satisfying measure of pattern similarity