GLCM Feature-Based Texture Image Classification Using Support Vector Machine

Abstract

AbstractTexture is one of the important characteristics of an image, and its features can be used to identify specific region of interest from the image. Texture features can be extracted for any kind of images like RGB, monochrome, aerial, satellite images. This chapter describes an approach for texture image classification based on Gray-level co-occurrence matrix (GLCM) features and machine learning algorithm like support vector machine. Ten different GLCM features were extracted and fed as input to support vector machine for classification. The proposed method is trained and tested with dataset collected from center for image analysis, Swedish University. In recent days, machine learning (ML) methods are highly used to mimic the complex mathematical expressions of texture features. The main objective of this chapter is to demonstrate the state of the art of ML models in texture image prediction and to give insight into the most suitable models.KeywordsSupport vector machineTexture imageMachine learningConfusion MatrixGray-level co-occurrence matrix