A Novel Approach of Co­Occurrence Matrix for Gangue Separation from Iron

Abstract

Ore sorting is a useful tool to remove gangue material from the ore and increase the quality of the ore. The vast developments in the area of artificial intelligence allows fast processing of full color digital images for the preferred investigations. In this paper, a novel approach to categorize the ores of iron feed has been proposed based on analyzing color­texture features using two different approaches, based on extensions of the co­occurrence matrix method. In the first method, co­occurrence matrices were computed both between and within the color bands and the second method used joint color­texture features. The dimensions of the image features were reduced by applying Euclidean Distance ranking. A multi­layer perceptron neural network model was used as a mapping function to classify the material.