A robust image descriptor-local radial grouped invariant order pattern

Abstract

Sorted-based LBP variants have been validated as effective grayscale inverse image classification methods. However, most of these methods encode the order of sampling points at the same scale and thus suffer from two problems: 1) Ignoring inter-scale correlation leads to descriptors that are not resistant to real scene changes. 2) The inherent flaws of sorted encoding cause descriptors to discriminate complex texture structures, showing low discriminability. To address these problems, we design the new scale-structure model and region encoding to realize a more robust and discriminative representation called Local Radial Grouped Invariant Order Pattern (LRGIOP). LRGIOP can effectively distinguish texture details in real scenes while resisting various complex imaging conditions. Experiments on several image databases show that the LRGIOP descriptor achieves state-of-the-art classification results under linear or even nonlinear grayscale-inversion transformations.