Texture representation and retrieval using the causal autoregressive model

In this paper we propose to revisit the well-known autoregressive model (AR) as a texture representation model. We consider the AR model with causal neighborhoods. First, we will define the AR model and discuss briefly the parameters estimation process. Then, we will present the synthesis algorithm and we will show some experimental results. A perceptual interpretation of the AR estimated parameters will be then proposed and discussed. In particular, a computational measure to estimate the degree of randomness/regularity of textures is proposed. The set of the estimated parameters will be then applied in content-based image retrieval (CBIR) to model texture content and experimental results are shown. Benchmarking, using the precision/recall measures conducted on the well-known Brodatz database, shows interesting results.