Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval

One of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems. In applications such as database retrieval, it is important that image features used in pattern comparison provide good measures of image perceptual similarities. In this paper, we present an image model with a new set of features that address the challenge of perceptual similarity. The model is based on the 2-D Wold decomposition of homogeneous random elds. The three resulting mutually orthogonal subbelds have perceptual properties which can be described as \periodicity", \directional-ity", and \randomness", approximating what are indicated to be the three most important dimensions of human texture perception. The method presented here improves upon earlier Wold-based models in its tolerance to a variety of local inho-mogeneities which arise in natural textures and its invariance under image transformation such as rotation. An image retrieval algorithm based on the new texture model is presented. Diierent types of image features are aggregated for similarity comparison by using a Bayesian probabilistic approach. The eeectiveness of the Wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well-known pattern recognition methods. The Wold model appears to ooer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria. Examples of natural scene Wold texture modeling are also presented.