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Rock Textures Classification Based on Textural and Spectral Features

Authors: Somkait Udomhunsakul, Tossaporn Kachanubal

Abstract:

In this paper, we proposed a method to classify each type of natural rock texture. Our goal is to classify 26 classes of rock textures. First, we extract five features of each class by using principle component analysis combining with the use of applied spatial frequency measurement. Next, the effective node number of neural network was tested. We used the most effective neural network in classification process. The results from this system yield quite high in recognition rate. It is shown that high recognition rate can be achieved in separation of 26 stone classes.

Keywords: Neural Network, texture classification, SfM, rock texture classification

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071600

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References:


[1] M. Partio, B. Cramariuc, M. Gabbouj, and A. Visa, "Rock texture retrieval using gray level co-occurrence matrix" Norsig2002, Oct 2002.
[2] L. Lepisto, I. Kunttu, J. Autio and A. Visa, "Rock image classification using non-homogeneous textures and spectral imaging". WSCD-2003, Feb 2003.
[3] Haralick, R.M., Shanmugam, L., Dinstein, "Textural features for image classification", IEEE Trans. Systems, Manufact, Cybernet., Vol. 3, Issue 6, pp. 610-621, 1973.
[4] Topi Mäenpää, Matti Pietikää, "Classification with color and texture: jointly or separately", Pattern Recognition 37, Issue 8, pp. 1629-1640, August 2004.
[5] L.I. Smith, A tutorial on Principle Component Analysis, Feb 2002.
[6] A.McAndrew, "Introduction to Digital Image Processing with Matlab", Thomson, 2004.
[7] S. Grgic, M.Grgic, and M. Mrak, "Reliability of Objective Picture Quality Measures Measurement", Journal of Electrical Engineering, Vol. 55, No. 1-2, pp.3-10, 2004.
[8] S. Walczak, N. Cerpa, "Heuristic principles for the design of artificial neural networks", Information and Software Technology 41, pp. 107- 117, 1999.
[9] S. B. Park, J. W. Lee, S. K. Kim, "Content-based image classification using neural network", Pattern Recognition Letters 25, pp. 287-300, 2004.
[10] N. Wanas, G. Auda, M. S. Kamel, F. Karray, "On the Optimal Number of Hidden Nodes in a Neural Network", IEEE Canadian Conference, Volume 2, pp. 918-921, 1998.
[11] V. DeBrunner, M. Kadiyala, "Texture Classification Using Wavelet Transform", IEEE Trans on Circuits and Systems, Volume 2, pp. 1053- 1056, Aug 1999.