Abstract
The two groups of popularly used texture analysis techniques for classification problems are the statistical and signal processing methods. In this paper, we propose to use a signal processing method, the Gabor filters to produce the feature images, and a statistical method, the covariance matrix to produce a set of features which show the statistical information of frequency domain. The experiments are conducted on 32 textures from the Brodatz texture dataset. The result that is obtained for the use of 24 Gabor filters to generate a 24 × 24 covariance matrix is 91.86%. The experiment results show that the use of Gabor filters as the feature image is better than the use of edge information and co-occurrence matrices.
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References
Tuceryan, M., Jain, A.K.: Texture analysis. In: The Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 207–248. World Scientific Publishing, Singapore (1998)
Lew, Y.L.: Design of an Intelligent Wood Recognition System for the Classification of Tropical Wood Species. M.E. Thesis, Universiti Teknologi Malaysia (2005)
Tou, J.Y., Lau, P.Y., Tay, Y.H.: Computer Vision-based Wood Recognition System. In: Proc. Int’l Workshop on Advanced Image Technology, Bangkok (2007)
Partio, M., Cramariuc, B., Gabbouj, M., Visa, A.: Rock Texture Retrieval using Gray Level Co-occurrence Matrix. In: Proc. of 5th Nordic Signal Processing Symposium (2002)
Yap, W.H., Khalid, M., Yusof, R.: Face Verification with Gabor Representation and Support Vector Machines. In: IEEE Proc. of the First Asia International Conference on Modeling and Simulation (2007)
Chen, Y.Q.: Novel techniques for image texture classification. PhD Thesis, University of Southampton, United Kingdom (1995)
Tou, J.Y., Tay, Y.H., Lau, P.Y.: Gabor Filters and Grey-level Co-occurrence Matrices in Texture Classification. In: MMU International Symposium on Information and Communications Technologies, Petaling Jaya (2007)
Nixon, M., Aguando, A.: Feature extraction and Image Processing. Butterworth-Heinemann, Great Britain (2002)
Tuzel, O., Porikli, F., Meer, P.: Region Covariance: A Fast Descriptor for Detection and Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 697–704. Springer, Heidelberg (2006)
Forstner, W., Moonen, B.: A Metric for Covariance Matrices. Technical report, Dept. of Geodesy and Geoinformatics, Stuttgart University (1999)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1996)
Ojala, T., Pietikainen, M., Kyllonen, J.: Gray level Cooccurrence Histograms via Learning Vector Quantization. In: Proc. 11th Scandinavian Conference on Image Analysis, pp. 103–108 (1999)
Tou, J.Y., Tay, Y.H., Lau, P.Y.: One-dimensional Grey-level Co-occurrence Matrices for Texture Classification. In: International Symposium on Information Technology, Kuala Lumpur, vol. 3, pp. 1592–1597 (2008)
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Tou, J.Y., Tay, Y.H., Lau, P.Y. (2009). Gabor Filters as Feature Images for Covariance Matrix on Texture Classification Problem. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_91
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DOI: https://doi.org/10.1007/978-3-642-03040-6_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
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