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In this paper we use machine learning to study the application of LocalTchebichef Moments (LTM) to the problem of texture classification. The originalLTM 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 relationshipbetween the moment values of different orders, producing a histogram similar tothose of Local Binary Pattern (LBP) based methods. The original method was notfully tested with large datasets, and there are several parameters that shouldbe 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 themodification of the parameters that can affect its performance. Using foursubsets from the Outex dataset (a popular benchmark for texture analysis), weused Random Forests to create models and to classify texture images, recordingthe standard metrics for each classifier. We repeated the process using severalvariations of the LBP method for comparison. This allowed us to find the bestcombination of orders and weights for the LTM method for textureclassification.

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