Probabilistic Based Rock Texture Classification

The classification of natural images is an essential task in computer vision and pattern recognition applications. Rock images are the typical example of natural images, and their analysis is of major importance in rock industries and bedrock investigations. Rocks are mainly classified into three types. They are Igneous, Metamorphic and Sedimentary. They are further classified into Andesite, Basalt, Amphibolite, Granite, Breccia, Coal and etc... In this project classification is done in three subdivisions. First the given rock image is classified into major class. Next it is classified into subclass. Finally the group of coal images is segmented and classified using Tamura features, Probabilistic Latent Semantic Analysis (PLSA) and Sum of Square Difference classifier. Rock image classification is based on specific visual descriptors extracted from the images. Using these descriptors images are divided into classes according to their visual similarity. This project deals with the rock image classification using two approaches. Firstly the textural features of the rock images are calculated by applying Tamura features extraction method. The Tamura features are Coarseness, Contrast, Directionality, Line likeness, Roughness and Regularity, Smoothness and Angular second moments. In next step calculated Tamura features are applied to Probabilistic Latent Semantic Analysis (PLSA) to generate a topic model. This topic model is applied to SSD classifier to classify the rock image into one of the major class. Similarly the rock textures are classified into subclass, and the group of coal images is segmented and classified. This method is compared with Gray Level Co-occurrence Matrix (GLCM) method and Color Co-occurrence Matrix method. This method gives a better accuracy when compared to those methods. This technique can readily be applied to automatically classify the rocks in such fields of rock industries and bedrock investigations.