Granite Classification: An Industrial Application to Color Texture Classification - Shivashankar, S; Kagale, MR (International Journal of Computer Sciences and Engineering, 2025)
Color texture classification is a vital step for describing objects in natural scenes. A novel method is proposed to construct a histogram based on intensity and color channel neighborhood for the color texture classification. The goal of this paper is to explore the suitability of the histogram constructed using the intensity and color channel neighborhood relationship method in automatic classification of granite textures as an industrial application. Experimental tests are conducted on the images from VisTex database. Texture classification is performed using K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classification methods. The average classification accuracy 97.93% is obtained for K-NN classification method, where as 100% average classification accuracy is achieved for SVM classification method. Further, experimentations are performed on MondialMarmi database of granite tiles to prove the potential of the proposed method in an industrial application. The classification results demonstrate that proposed method has improved classification accuracy as compared to other color texture classification methods. The results prove that proposed method using SVM is a powerful classification method for classifying granite textures.