High-Order MGRF Models for Contrast/Offset Invariant Texture Retrieval

Local ordinal signal relations, such as local binary or ternary patterns (LBP/LTP), and their statistics are promising texture descriptors due to their invariance to frequent in practice spatially variant contrast / offset deviations that preserve image appearance. This paper extends conventional LBP/LTP-based classifiers towards learning, rather than prescribing characteristic shapes, sizes, and numbers of such patterns in order to facilitate accurate image query based texture retrieval. The proposed learning and retrieval framework models a query image as a sample from a high-order Markov-Gibbs random field (MGRF) and uses the patterns learned and their training statistics to classify candidate images in a certain database and retrieve samples, which are similar to the query texture. Analytical approximations of the model parameters guide selecting characteristic patterns of a given order, the higher order patterns being learned on the basis of the already found lower order ones. Comparative experiments on four texture databases confirmed that the learned models with multiple high-order (from the 3rd to 8th order) LTPs, often just from the 4th order, consistently outperform the conventional prescribed 8th-order fixed-shape LBP/LTPs.