ROCK IMAGE RETRIEVAL AND CLASSIFICATION BASED ON GRANULARITY (original) (raw)

In this paper, we consider the use of texture granularity in the classification and retrieval of natural rock images. In rock science, the rock images are nowadays stored into large image databases. In the images, there often occur large grains which differ clearly from rock texture. The purpose of this work is to find grain rock images from the database. We present two approaches to this purpose: classification and retrieval approach. In both approaches, the grains of desired color and size are recognized from the database images using color analysis combined with morphological tools. The experimental results show that using our method, the images with grain can be distinguished from the other rock images.