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Texture Classification Using Sparse Frame-Based Representations


A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance.


  1. 1.

    Tuceryan M, Jain AK: Texture analysis. In Handbook of Pattern Recognition and Computer Vision. 2nd edition. Edited by: Chen CH, Pau LF, Wang PSP. World Scientific, Singapore; 1998:207–248. chapter 2.1

    Google Scholar 

  2. 2.

    Dekker RJ: Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(9):1950–1958. 10.1109/TGRS.2003.814628

    Article  Google Scholar 

  3. 3.

    Kundu MK, Acharyya M: M-band wavelets: application to texture segmentation for real life image analysis. International Journal of Wavelets, Multiresolution and Information Processing 2003, 1(1):115–149. 10.1142/S0219691303000074

    Article  Google Scholar 

  4. 4.

    Mendoza F, Aguilera JM: Application of image analysis for classification of ripening bananas. Journal of Food Science 2004, 69(9):471–477.

    Article  Google Scholar 

  5. 5.

    Arivazhagan S, Ganesan L: Automatic target detection using wavelet transform. EURASIP Journal on Applied Signal Processing 2004, 2004(17):2663–2674. 10.1155/S1110865704408208

    MATH  Google Scholar 

  6. 6.

    Singh S, Singh M: A dynamic classifier selection and combination approach to image region labelling. Signal Processing Image Communication 2005, 20(3):219–231. 10.1016/j.image.2004.11.006

    MathSciNet  Article  Google Scholar 

  7. 7.

    Unser M: Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 1995, 4(11):1549–1560. 10.1109/83.469936

    MathSciNet  Article  Google Scholar 

  8. 8.

    Liapis S, Sifakis E, Tziritas G: Colour and texture segmentation using wavelet frame analysis, deterministic relaxation, and fast marching algorithms. Journal of Visual Communication and Image Representation 2004, 15(1):1–26. 10.1016/S1047-3203(03)00025-7

    Article  Google Scholar 

  9. 9.

    McLean GF: Vector quantization for texture classification. IEEE Transactions on Systems, Man, and Cybernetics 1993, 23(3):637–649. 10.1109/21.256539

    Article  Google Scholar 

  10. 10.

    Kohonen T: The self-organizing map. Proceedings of the IEEE 1990, 78(9):1464–1480. 10.1109/5.58325

    Article  Google Scholar 

  11. 11.

    Randen T, Husøy JH: Filtering for texture classification: a comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 1999, 21(4):291–310. 10.1109/34.761261

    Article  Google Scholar 

  12. 12.

    Diamantini C, Spalvieri A: Quantizing for minimum average misclassification risk. IEEE Transactions on Neural Networks 1998, 9(1):174–182. 10.1109/72.655039

    Article  Google Scholar 

  13. 13.

    Malpica N, Ortuño JE, Santos A: A multichannel watershed-based algorithm for supervised texture segmentation. Pattern Recognition Letters 2003, 24(9–10):1545–1554. 10.1016/S0167–8655(02)00393-8

    Article  Google Scholar 

  14. 14.

    Li S, Kwok JT, Zhu H, Wang Y: Texture classification using the support vector machines . Pattern Recognition 2003, 36(12):2883–2893. 10.1016/S0031-3203(03)00219-X

    Article  Google Scholar 

  15. 15.

    Randen T, Husøy JH: Texture segmentation using filters with optimized energy separation. IEEE Transactions on Image Processing 1999, 8(4):571–582. 10.1109/83.753744

    Article  Google Scholar 

  16. 16.

    Kim KI, Jung K, Park SH, Kim HJ: Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(11):1542–1550. 10.1109/TPAMI.2002.1046177

    Article  Google Scholar 

  17. 17.

    Natarajan BK: Sparse approximate solutions to linear systems. SIAM Journal on Computing 1995, 24(2):227–234. 10.1137/S0097539792240406

    MathSciNet  Article  Google Scholar 

  18. 18.

    Davis G: Adaptive nonlinear approximations, Ph.D. dissertation. New York University, New York, NY, USA; 1994.

    Google Scholar 

  19. 19.

    Mallat SG, Zhang Z: Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 1993, 41(12):3397–3415. 10.1109/78.258082

    Article  Google Scholar 

  20. 20.

    Pati YC, Rezaiifar R, Krishnaprasad PS: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Proceedings of 27th IEEE Asilomar Conference on Signals, Systems and Computers, November 1993, Pacific Grove, Calif, USA 1: 40–44.

    Article  Google Scholar 

  21. 21.

    Chen S, Wigger J: Fast orthogonal least squares algorithm for efficient subset model selection. IEEE Transactions on Signal Processing 1995, 43(7):1713–1715. 10.1109/78.398734

    Article  Google Scholar 

  22. 22.

    Gharavi-Alkhansari M, Huang TS: A fast orthogonal matching pursuit algorithm. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), May 1998, Seattle, Wash, USA 3: 1389–1392.

    Google Scholar 

  23. 23.

    Cotter SF, Adler R, Rao RD, Kreutz-Delgado K: Forward sequential algorithms for best basis selection. IEE Proceedings—Vision, Image and Signal Processing 1999, 146(5):235–244. 10.1049/ip-vis:19990445

    Article  Google Scholar 

  24. 24.

    Skretting K, Husøy JH: Partial search vector selection for sparse signal representation. Proceedings of IEEE Norwegian Symposium on Signal Processing (NORSIG '03), October 2003, Bergen, Norway

    Google Scholar 

  25. 25.

    Heeger DJ, Bergen JR: Pyramid-based texture analysis/synthesis. Proceedings of IEEE International Conference on Image Processing (ICIP '95), October 1995, Washington, DC, USA 3: 648–651.

    Article  Google Scholar 

  26. 26.

    Portilla J, Simoncelli EP: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 2000, 40(1):49–71. 10.1023/A:1026553619983

    Article  Google Scholar 

  27. 27.

    Paget R: Strong Markov random field model. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(3):408–413. 10.1109/TPAMI.2004.1262338

    Article  Google Scholar 

  28. 28.

    Paget R: Nonparametric Markov random field models for natural texture images, M.S. thesis. Ph.D. dissertation, University of Queensland, Queensland, Australia, 1999, available at

    Google Scholar 

  29. 29.

    Engan K, Aase SO, Husøy JH: Method of optimal directions for frame design. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 5: 2443–2446.

    Google Scholar 

  30. 30.

    Skretting K: Sparse signal representation using overlapping frames, M.S. thesis. Ph.D. dissertation, Norwegian University of Science and Technology, Trondheim, Norway, October 2002, available at

    Google Scholar 

  31. 31.

    Gersho A, Gray RM: Vector Quantization and Signal Compression. Kluwer Academic, Norwell, Mass, USA; 1992.

    Google Scholar 

  32. 32.

    Laine A, Fan J: Frame representations for texture segmentation. IEEE Transactions on Image Processing 1996, 5(5):771–780. 10.1109/83.499915

    Article  Google Scholar 

  33. 33.

    Van Nevel A: Texture classification using wavelet frame decompositions. Proceedings of 31st IEEE Asilomar Conference on Signals, Systems and Computers, November 1997, Pacific Grove, Calif, USA 1: 311–314.

    Google Scholar 

  34. 34.

    Bolcskei H, Hlawatsch F, Feichtinger HG: Frame-theoretic analysis of oversampled filter banks. IEEE Transactions on Signal Processing 1998, 46(12):3256–3268. 10.1109/78.735301

    Article  Google Scholar 

  35. 35.

    Unser M, Eden M: Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering. IEEE Transactions on Systems, Man, and Cybernetics 1990, 20(4):804–815. 10.1109/21.105080

    Article  Google Scholar 

  36. 36.

    Acharyya M, De RK, Kundu MK: Extraction of features using M-band wavelet packet frame and their neuro-fuzzy evaluation for multitexture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(12):1639–1644. 10.1109/TPAMI.2003.1251158

    Article  Google Scholar 

  37. 37.

    Ojala T, Valkealahti K, Oja E, Pietikäinen M: Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition 2001, 34(3):727–739. 10.1016/S0031-3203(00)00010-8

    Article  Google Scholar 

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Correspondence to Karl Skretting.

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Skretting, K., Husøy, J. Texture Classification Using Sparse Frame-Based Representations. EURASIP J. Adv. Signal Process. 2006, 052561 (2006).

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  • Test Image
  • Iterative Procedure
  • Texture Classification
  • Representation Error
  • Image Block