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RGB - Based Color Texture Image Classification Using Anisotropic Diffusion and LDBP

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8875))

Abstract

In this paper, a novel color texture image classification based on RGB color space using anisotropic diffusion, and local directional binary patterns (LDBP) is introduced. Traditionally, RGB color space is widely used in digital images and hardware. RGB color space is applied to obtain more accurate color statistics for extracting features. According to characteristic of anisotropic diffusion, image is decomposed into cartoon approximation; further the texture approximation is obtained by subtracting the original image and cartoon approximation. Then, texture features of image are obtained by applying LDBP co-occurrence matrix parameters on texture approximation. LDA is used to enhance the class seperability. After feature extraction, k-NN classifier is used to classify texture classes by the extracted features. The proposed method is evaluated on Oulu database. Experimental results demonstrate the proposed method is better and more correct than RGB based color texture image classification methods in the literature.

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References

  1. Tuceryan, M., Jain, A.K.: Texture Analysis. In: Handbook of Pattern Recognition and Computer Vision, pp. 235–276 (1993)

    Google Scholar 

  2. Haralick, R.M.: Statistical and Structural Approaches to Texture. Proceedings of IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  3. Zhang, J., Tan, T.: Brief Review of Invariant Texture Analysis Methods. Pattern Recognition 35(3), 735–747 (2002)

    Article  MATH  Google Scholar 

  4. Petrou, M., García-Sevilla, P.: Image Processing Dealing with Texture. Wiley (2006)

    Google Scholar 

  5. Mirmehdi, M., Xie, X.H., Suri, J.: Handbook of Texture Analysis. World Scientific (2008)

    Google Scholar 

  6. Chen, Y.Q., Nixon, M.S., Thomas, D.W.: Statistical Geometrical Features for Texture Classification. Pattern Recognition 28(4), 537–552 (1995)

    Article  Google Scholar 

  7. Vilnrotter, F.M., Nevatia, R., Price, K.E.: Structural Analysis of Natural Textures. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(1), 76–89 (1986)

    Article  Google Scholar 

  8. Azencott, R., Wang, J.-P., Younes, L.: Texture Classification using Windowed Fourier Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 148–153 (1997)

    Article  Google Scholar 

  9. Julesz, B.: Experiments in the Visual Perception of Texture. Scientific American 232(4), 34–43 (1975)

    Article  Google Scholar 

  10. Keller, J.M., Chen, S., Crownover, R.M.: Texture Description and Segmentation Through Fractal Geometry. Computer Vision Graphics and Image Processing 45(2), 150–166 (1989)

    Article  Google Scholar 

  11. Sengur, A., Turkoglu, I., Ince, M.C.: Wavelet Packet Neural Networks for Texture Classification. Expert Systems with Applications 32(2), 527–533 (2007)

    Article  Google Scholar 

  12. Sengur, A.: Wavelet Transform and Adaptive Neuro-fuzzy Inference System for Color Texture Classification. Expert Systems with Applications 34(3), 2120–2128 (2008)

    Article  Google Scholar 

  13. Karabatak, M., Ince, M.C., Sengur, A.: Wavelet Domain Association Rules for Efficient Texture Classification. Applied Soft Computing 11(1), 32–38 (2011)

    Article  Google Scholar 

  14. Daugman, J., Downing, C.: Gabor Wavelets for Statistical Pattern Recognition. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 414–419. MIT Press, Cambridge (1995)

    Google Scholar 

  15. Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency and Orientation Optimized by Two-Dimensional Visual Cortical Filters. Journal of the Optical Society of America 2(7), 1160–1169 (1985)

    Article  Google Scholar 

  16. Mayhew, J.E.W., Frisby, J.P.: Texture Discrimination and Fourier Analysis in Human Vision. Nature 275, 438–439 (1978)

    Article  Google Scholar 

  17. Horng, M.H.: Texture Feature Coding Method for Texture Classification. Opt. Eng. 42(1), 228–238 (2003)

    Article  Google Scholar 

  18. Liang, J., Zhao, X., Xu, R., Kwan, C., Chang, C.-I.: Target Detection with Texture Feature Coding Method and Support Vector Machines. In: Proc. ICASSP, Montreal, QC, Canada, pp. II-713–II-716 (2004)

    Google Scholar 

  19. Torrione, P., Collins, L.M.: Texture Features for Antitank Landmine Detection using Ground Penetrating Radar. IEEE Trans. Geosci. Remote Sens. 45(7), 2374–2382 (2007)

    Article  Google Scholar 

  20. Arivazhagan, S., Ganesan, L.: Texture Classification using Wavelet Transform. Pattern Recognition Letters 24(9-10), 1513–1521 (2003)

    Article  MATH  Google Scholar 

  21. Selvan, S., Ramakrishnan, S.: SVD-based Modeling for Image Texture Classification using Wavelet Transformation. IEEE Transactions on Image Processing 16(11), 2688–2696 (2007)

    Article  MathSciNet  Google Scholar 

  22. Turkoglu, I., Avci, E.: Comparison of Wavelet-SVM and Wavelet-Adaptive Network based Fuzzy Inference System for Texture Classification. Digital Signal Processing 18(1), 15–24 (2008)

    Article  Google Scholar 

  23. Ghafoor, A.: Multimedia Database Management System. ACM Comput. Surv. 27(4), 593–598 (1995)

    Article  Google Scholar 

  24. Hiremath, P.S., Bhusnurmath, R.A.: Texture Image Classification using Nonsubsampled Contourlet Transform and Local Directional Binary Patterns. Int. Journal of Applied Research in Computer Science and Software Engineering 3(7), 819–827 (2013)

    Google Scholar 

  25. Shivashankar, S., Hiremath, P.S.: PCA plus LDA on Wavelet Co-Occurrence Histogram Features for Texture Classification. Int. Journal of Machine Intelligence 3(4), 302–306 (2011)

    Google Scholar 

  26. Hiremath, P.S., Bhusnurmath, R.A.: Nonsubsampled Contourlet Transform and Local Directional Binary Patterns for Texture Image Classification Using Support Vector Machine. Int. Journal of Engineering Research and Technology 2(10), 3881–3890 (2013)

    Google Scholar 

  27. Hiremath, P.S., Bhusnurmath, R.A.: A Novel Approach to Texture Classification using NSCT and LDBP. IJCA Special Issue on Recent Advances in Information Technology (NCRAIT 2014) 3, 36–42 (2014) ISBN-973-93-80880-08-3

    Google Scholar 

  28. Sengur, A.: Color Texture Classification using Wavelet Transform and Neural Network Ensembles. The Arabian Journal for Science and Engineering 34(2B), 491–502 (2009)

    Google Scholar 

  29. Chang, J.-D., Yu, S.-S., Chen, H.-H., Tsai, C.-S.: HSV-based Color Texture Image Classification using Wavelet Transform and Motif Patterns. Journal of Computers 20(4), 63–69 (2010)

    Google Scholar 

  30. Abdulmunim Matheel, E.: Color Texture Classification using Adaptive Discrete Multiwavelets Transform. Eng. & Tech. Journal 30(4), 615–627 (2012)

    Google Scholar 

  31. Lindeberg, T.: Scale-space. In: Wah, B. (ed.) Encyclopedia of Computer Science and Engineering, EncycloCSE 2008, vol. 4, pp. 2495–2504. John Wiley and Sons, Hoboken (2008)

    Google Scholar 

  32. Witkin, P.: Scale-Space Filtering. In: Int. Joint Conference on Artificial Intelligence, pp. 1019–1022 (1983)

    Google Scholar 

  33. Perona, P., Malik, J.: Scale Space and Edge Detection using Anisotropic Diffusion. In: Proc. IEEE Comp. Soc. Workshop on Computer Vision, Miami Beach, November 30-December 2, pp. 16–22. IEEE Computer Society Press, Washington (1987)

    Google Scholar 

  34. Perona, P., Malik, J.: Scale Space and Edge Detection using Anisotropic Diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)

    Article  Google Scholar 

  35. Duda, R.O., Hart, P.E.: Stork: Pattern Classification. Wiley Publications, New York (2001)

    Google Scholar 

  36. Internet: University of Oulu texture database (2005), http://www.outex.oulu.fi/outex.php

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Author information

Authors and Affiliations

  1. Department of P.G. Studies and Research in Computer Science, Gulbarga University, Gulbarga, 585106, India

    Prakash S. Hiremath & Rohini A. Bhusnurmath

Authors
  1. Prakash S. Hiremath
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  2. Rohini A. Bhusnurmath
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Automation, Indian Institute of Science, 560 012, Bangalore, India

    M. Narasimha Murty

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, NSW, Australia

    Xiangjian He

  3. Department of Computer and Information Sciences, University of Hyderabad, 500046, Hyderabad, India

    Raghavendra Rao Chillarige

  4. LIP6, University Pierre et Marie Curie, 4 Place Jussieu, 75005, Paris, France

    Paul Weng

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Hiremath, P.S., Bhusnurmath, R.A. (2014). RGB - Based Color Texture Image Classification Using Anisotropic Diffusion and LDBP. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-13365-2_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13364-5

  • Online ISBN: 978-3-319-13365-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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