Multispectral and joint colour-texture feature extraction for ore-gangue separation

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 allow fast processing of full color digital images for the preferred investigations. Three different approaches to color texture analysis were used for the classification of associated gangue from limestone and iron ore. All the methods were based on extensions of the co-occurrence matrix method. The first approach was a correlation method, in which co-occurrence matrices are computed both between and within the color bands. In the second approach, joint color-texture features, where color features were extracted from chrominance information and texture features were extracted from luminance information of the color bands. The last approach used grey scale texture features computed on a quantized color image. Results showed that the joint color-texture method was 98% accurate for limestone and 98.4% for iron ore gangue classification. It was further observed that the features showed better accuracy with 64 grey levels quantization.