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.

Color texture classification, Granite classification, Industrial application, Histogram features, Classification methods

[1] A. Agarwal, S. S. Bhadouria, “An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, No. 2, pp.56-62, 2017.
[2] S. K. Badugu, R. K. Kontham, V. K. Vakulabharanam, B. Prajna, “Calculation of Texture Features for Polluted Leaves”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, No. 1, pp.11-21, 2018.
[3] M. Turtinen, M. Pietikainen, O. Silvén, T. Maenpaa, M. Niskanen, “Paper Characterisation by Texture using Visualisation-based Training”, International Journal of Advanced Manufacturing Technology, Vol. 22, No.11-12, pp.890-898, 2003.
[4] S. Kukkonen, H. A. Kaelviaeinen, J. P. Parkkinen, “Color Features for Quality Control in Ceramic Tile Industry”, Optical Engineering, Vol. 40, No.2, pp.170-178, 2001.
[5] M. Bennamoun, A. Bodnarova, “Digital Image Processing Techniques for Automatic Textile Quality Control”, Systems Analysis Modelling Simulation, Vol. 43, No.11, pp.1581-1614, 2003.
[6] G. Paschos, M. Petrou, “Histogram Ratio Features for Color Texture Classification”, Pattern Recognition Letters, Vol. 24, No.1-3, pp.309-314, 2003.
[7] A. Porebski, V Vandenbroucke, V Macaire, “Haralick Feature Extraction from LBP Images for Color Texture Classification”, In the workshop of the 2008, IEEE on Image Processing Theory, Tools and Applications, (IPTA 2008), pp. 1-8, Nov.2008.
[8] S. Banerji, A.Sinha, C. Liu, “New Image Descriptors Based on Color, Texture, Shape, and Wavelets for Object and Scene Image Classification”, Neurocomputing, Vol. 117, pp.173-185, 2013.
[9] C. Palm, “Color Texture Classification by Integrative Co-occurrence Matrices”, Pattern Recognition, Vol. 37No.5, pp.965-976, 2004.
[10] F. Bianconi, A. Fernández, E. González, F. Ribas, “Texture Classification through Combination of Sequential Colour Texture Classifiers”, In Iberoamerican Congress on Pattern Recognition, Springer, Berlin, Heidelberg, pp. 231-240, November, 2007.
[11] S. Chindaro, K. Sirlantzis, F. Deravi, “Texture Classification System using Colour space Fusion”, Electronics Letters, Vol. 41, No.10, pp.589-590, 2005.
[12] M. Benco, R. Hudec, P. Kamencay, M. Zachariasova, S. Matuska, “An Advanced Approach to Extraction of Colour Texture Features based on GLCM”, International Journal of Advanced Robotic Systems, Vol. 11, No.7, pp.104, 2014.
[13] C. Cusano, P. Napoletano, R. Schettini, “Evaluating Color Texture Descriptors under Large Variations of Controlled Lighting Conditions”, JOSAA, Vol. 33, No.1, pp.17-30, 2016.
[14] E. Cernadas, M. Fernández-Delgado, E. González-Rufino, P. Carrión, “Influence of Normalization and Color Space to Color Texture Classification”, Pattern Recognition, Vol. 61, pp.120-138, 2017.
[15] S. Shivashankar M. R. Kagale, P. S. Hiremath, “Inter Intensity and Color Channel Co-occurrence Histogram for Color Texture Classification”, In the Proceedings of the 2017 Springer Third International Conference on Cognitive Computing and Information Processing (CCIP, Dec 2017), Bangaluru, India, CCIS 801, pp.182-190, 2018.
[16] R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification”, John Wiley & Sons, 2012.
[17] M. Zhao, J. An, H. Li, J. Zhang, S. T. Li, X. M. Li, L. Tao, “Segmentation and Classification of Two-Channel C. Elegans Nucleus-Labeled Fluorescence Images”, BMC Bioinformatics, Vol. 18, No.1, pp.412, 2017.
[18] G. Anthony, H. Gregg, M. Tshilidzi, “Image Classification Using SVMs: One-against-One vs One-against-All”, arXiv preprint arXiv:0711.2914, 2007.
[19] VisTex, “Vision Texture Database”, Vision and Modeling Group, MIT Media Laboratory, http://wwwwhite.media.mit.edu/vismod/imagery/VisionTexture/vistex.html, 1995.
[20] F. Bianconi, A. Fernández, E. González, D. Caride, A. Calviño, “Rotation-Invariant Colour Texture Classification through Multilayer CCR”, Pattern Recognition Letters, Vol. 30, No.8, pp. 765-773, 2009.
[21] F. Bianconi, E. González, A. Fernández, S. A. Saetta, “Automatic Classification of Granite Tiles Through Colour and Texture Features”, Expert Systems with Applications, Vol. 39, No. 12, pp.11212-11218, 2012.
[22] E. González, F. Bianconi, M. X. Álvarez, S. A. Saetta, “Automatic Characterization of the Visual Appearance of Industrial Materials through Colour and Texture Analysis: An Overview of Methods and Applications. Advances in Optical Technologies, 2013.
[23] A. Fernández, O. Ghita, E. González, F. Bianconi, P. F. Whelan, “Evaluation of Robustness against Rotation of LBP, CCR and ILBP Features in Granite Texture Classification”. Machine Vision and Applications, Vol. 22, No.6, pp.913-926, 2011.
[24] A. Fernández, M. X. Álvarez, F. Bianconi, “Texture Description through Histograms of Equivalent Patterns”, Journal of Mathematical Imaging and Vision, vol. 45, No.1, pp.76-102, 2013.
[25] F. Bianconi, R. Bello, A. Fernández, E. González, “On Comparing Colour Spaces from a Performance Perspective: Application to Automated Classification of Polished Natural Stones”, In International Conference on Image Analysis and Processing, Springer, Cham, pp. 71-78, September 2015.
[26] MondialMarmi, “MondialMarmi: A Granite Image Database for Colour and Texture Analysis”, Version 1.1. Available online at http://webs.uvigo.es/antfdez/downloads.html, 2011.