Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition

TL;DR

This paper introduces a novel shape and texture recognition descriptor that combines edge direction matrixes with local binary patterns, demonstrating superior performance over existing methods on multiple benchmark datasets.

Contribution

It proposes a new combinative descriptor integrating EDMS and LBP, enhancing recognition accuracy by leveraging their complementary strengths.

Findings

Outperforms GLCM with EDMS, LBP, and moment invariants

Achieves superior accuracy on four benchmark datasets

Effective with multiple classifiers like random forest and neural networks

Abstract

Shapes and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from images via human knowledge and works. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction matrixes (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main idea behind this methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using multiple classifier approaches such as random forest and multi-layer perceptron neural network, the proposed combinative descriptor are compared with the state of the art combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS), LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPS image, Enghlishfnt and Arabic calligraphy . The experiments have shown the superiority of the introduced descriptor over the GLCM with EDMS, LBP and moment invariants and other well-known descriptor such as Scale Invariant Feature Transform from the literature.