Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters

TL;DR

This study evaluates the Local Tchebichef Moments method for texture classification, optimizing parameters through machine learning and comparing its performance with Local Binary Pattern variants on benchmark datasets.

Contribution

It introduces a flexible implementation of LTM, systematically characterizes its parameters, and identifies optimal configurations for improved texture classification accuracy.

Findings

LTM performance depends on kernel size, moment orders, and weights.

Optimized LTM parameters outperform baseline configurations.

LTM shows competitive results compared to LBP methods.

Abstract

In this paper we use machine learning to study the application of Local Tchebichef Moments (LTM) to the problem of texture classification. The original LTM method was proposed by Mukundan (2014). The LTM method can be used for texture analysis in many different ways, either using the moment values directly, or more simply creating a relationship between the moment values of different orders, producing a histogram similar to those of Local Binary Pattern (LBP) based methods. The original method was not fully tested with large datasets, and there are several parameters that should be characterised for performance. Among these parameters are the kernel size, the moment orders and the weights for each moment. We implemented the LTM method in a flexible way in order to allow for the modification of the parameters that can affect its performance. Using four subsets from the Outex dataset (a popular benchmark for texture analysis), we used Random Forests to create models and to classify texture images, recording the standard metrics for each classifier. We repeated the process using several variations of the LBP method for comparison. This allowed us to find the best combination of orders and weights for the LTM method for texture classification.