Pattern Analysis and Applications

, Volume 16, Issue 1, pp 1–18 | Cite as

Supervised texture classification: color space or texture feature selection?



The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times.


Texture classification Color spaces Feature selection Co-occurrence matrix 



This research is funded by “Pôle de Compétitivité Maud” and “Région Nord-Pas de Calais”.


  1. 1.
    Palm C (2004) Color texture classification by integrative co-occurrence matrices. Pattern Recognit 37(5):965–976CrossRefGoogle Scholar
  2. 2.
    Mäenpää T, Pietikäinen M (2004) Classification with color and texture: jointly or separately? Pattern Recognit 37(8):1629–1640CrossRefGoogle Scholar
  3. 3.
    Van Den Broek EL, Van Rikxoort EM (2004) Evaluation of color representation for texture analysis. In: Proceedings of the Belgium–Dutch Conference on artificial intelligence (BNAIC’04). Groningen, The NetherlandsGoogle Scholar
  4. 4.
    Busin L, Vandenbroucke N, Macaire L (2008) Color spaces and image segmentation. Adv Imag Electron Phys 151(2):65–168CrossRefGoogle Scholar
  5. 5.
    Van de Wouwer G, Scheunders P, Livens S, Van Dyck D (1999) Wavelet correlation signatures for color texture characterization. Pattern Recognit 32(3):443–451CrossRefGoogle Scholar
  6. 6.
    Singh M, Markou M, Singh S (2002) Colour image texture analysis: dependence on colour spaces. 16th International conference on pattern recognition, QuebecGoogle Scholar
  7. 7.
    Iakovidis D, Maroulis D, Karkanis S (2005) A comparative study of color-texture image features. In: Proceedings of 12th international workshop on systems, signals and image processing (IWSSIP’05). Chalkida, Greece, pp 203–207Google Scholar
  8. 8.
    Xu Q, Yang J, Ding S (2005) Color texture analysis using the wavelet-based hidden Markov model. Pattern Recognit Lett 26(11):1710–1719MathSciNetCrossRefGoogle Scholar
  9. 9.
    Arivazhagan S, Ganesan L, Angayarkanni V (2005) Color texture classification using wavelet transform. In: Proceedings of the sixth international conference on computational intelligence and multimedia applications (ICCIMA’05), pp 315–320Google Scholar
  10. 10.
    Hiremath PS, Shivashankar S, Pujari J (2006) Wavelet based features for color texture classification with application to CBIR. Int J Comput Sci Netw Secur 6(9):124–133Google Scholar
  11. 11.
    Sengur A (2007) Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Syst Appl 34(3):2120–2128CrossRefGoogle Scholar
  12. 12.
    Akhloufi MA, Maldague X, Larbi WB (2008) A new color-texture approach for industrial products inspection. J Multimedia 3(3):44–50Google Scholar
  13. 13.
    Nanni L, Lumini A (2009) Fusion of color spaces for ear authentication. Pattern Recognit 42(9):1906–1913MATHCrossRefGoogle Scholar
  14. 14.
    Chindaro S, Sirlantzis K, Deravi F (2005) Texture classification system using colour space fusion. Electron Lett 41:589–590CrossRefGoogle Scholar
  15. 15.
    Porebski A, Vandenbroucke N, Macaire L (2007) Iterative feature selection for color texture classification. In: Proceedings of the 14th IEEE international conference on image processing. San Antonio, Texas, USA, pp 509–512Google Scholar
  16. 16.
    Vandenbroucke N, Macaire L, Postaire J-G (2003) Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis. Comput Vis Image Understand 90(2):190–216CrossRefGoogle Scholar
  17. 17.
    D’Orazio T, Leo M (2010) A review of vision-based systems for soccer video analysis. Pattern Recognit 43(8):2911–2926CrossRefGoogle Scholar
  18. 18.
    Jain A, Zongker D (1997) Feature selection: evaluation, application and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158CrossRefGoogle Scholar
  19. 19.
    Tuceryan M, Jain AK (1998) Texture analysis. In: The handbook of pattern recognition and computer vision, chapter 2.1. World Scientific Publishing, Singapore, pp 207–248Google Scholar
  20. 20.
    Zheng C, Sun DW, Zheng L (2007) A new region-primitive method for classification of colour meat image texture based on size, orientation and contrast. Meat Sci 76(4):620–627MathSciNetCrossRefGoogle Scholar
  21. 21.
    Khotanzad A, Hernandez OJ (2006) A classification methodology for color textures using multispectral random field mathematical models. Math Comput Appl 11(2):111–120MATHGoogle Scholar
  22. 22.
    Hernandez OJ, Cook J, Griffin M, De Rama C, McGovern M (2005) Classification of color textures with random field models and neural networks. J Comput Sci Technol 5(3):150–157Google Scholar
  23. 23.
    Palm C, Lehmann TM (2002) Classification of color textures by Gabor filtering. Mach Graphics Vis Int J 11(2):195–219Google Scholar
  24. 24.
    Drimbarean A, Whelan PF (2001) Experiments in colour texture analysis. Pattern Recognit Lett 22(10):1161–1167MATHCrossRefGoogle Scholar
  25. 25.
    Arvis V, Debain C, Berducat M, Benassi A (2004) Generalization of the cooccurrence matrix for colour images: application to colour texture classification. Image Anal Stereol 23:63–72CrossRefGoogle Scholar
  26. 26.
    Martinez-Alajarin J, Luis-Delgado JD, Tomas-Balibrea LM (2005) Automatic system for quality-based classification of marble textures. IEEE Trans Syst Man Cybern 35(4):488–497CrossRefGoogle Scholar
  27. 27.
    Münzenmayer C, Volk H, Küblbeck C, Spinnler K, Wittenberg T (2002) Multispectral texture analysis using interplane sum- and difference-histograms, In: German Association for Pattern Recognition symposium, Zurich, Suisse. Springer, Berlin, pp 42–49Google Scholar
  28. 28.
    Porebski A, Vandenbroucke N, Macaire L (2008) Haralick feature extraction from LBP images for color texture classification. In: First international workshops on image processing theory, tools and applications (IPTA’08). Sousse, TunisiaGoogle Scholar
  29. 29.
    Mäenpää T, Viertola J, Pietikäinen M (2003) Optimising colour and texture features for real-time visual inspection. Pattern Anal Appl 6(3):169–175Google Scholar
  30. 30.
    Lopez F, Valiente JM, Prats JM, Ferrer A (2008) Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression. Pattern Recognit 41(5):1761–1772CrossRefGoogle Scholar
  31. 31.
    Qazi IUH, Alata O, Burie JC, Fernandez-Maloigne C (2010) Color spectral analysis for spatial structure characterization of textures in IHLS color space. Pattern Recognit 43(3):663–675MATHCrossRefGoogle Scholar
  32. 32.
    Xie X, Mirmehdi M (2007) TEXEMS: texture exemplars for defect detection on random textured surfaces. IEEE Trans Pattern Anal Mach Intell 29(8):1454–1464CrossRefGoogle Scholar
  33. 33.
    Porebski A, Vandenbroucke N, Macaire L (2009) Selection of color texture features from reduced size chromatic co-occurrence matrices. In: Proceedings of the IEEE international conference on signal and image processing applications (ICSIPA’09), Malaysia, pp 273–278Google Scholar
  34. 34.
    Rosenfeld A, Wang CY, Wu AY (1982) Multispectral texture. IEEE Trans Syst Man Cybern 12(1):79–84CrossRefGoogle Scholar
  35. 35.
    Haralick R, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  36. 36.
    Porebski A, Vandenbroucke N, Macaire L (2008) Neighborhood and Haralick feature extraction for color texture analysis. In: Proceedings of the 4th European conference on colour in graphics, imaging and vision (CGIV’08). Terrassa, Spain, pp 316–321Google Scholar
  37. 37.
    Ohta YI, Kanade T, Sakai T (1980) Color information for region segmentation. Comput Graph Image Process 13:222–241CrossRefGoogle Scholar
  38. 38.
    Ojala T, Mäenpää T, Pietikäinen M, Viertola J, Kyllönen J, Huovinen S (2002) Outex new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of the 16th international conference on pattern recognition (ICPR’02), Quebec, Canada, vol 1, pp 701–706.
  39. 39.
    Picard R, Graczyk C, Mann S, Wachman J, Picard L, Campbell L (1995) VisTex benchmark database of color textured images, Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge.
  40. 40.
    Plataniotis KN, Venetsanopoulos AN (2001) Color image processing and applications. Springer, BerlinGoogle Scholar
  41. 41.
    Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15:1119–1125CrossRefGoogle Scholar
  42. 42.
    Porebski A, Vandenbroucke N, Macaire L (2010) Comparison of feature selection schemes for color texture classification, In: The 2nd IEEE international workshops on image processing theory, tools and applications (IPTA’10), Paris, pp 32–37Google Scholar
  43. 43.
    Webster R (1971) Wilks’s criterion: a measure for comparing the value of general purpose soil classifications. J Soil Sci 22:254–260CrossRefGoogle Scholar
  44. 44.
    Pietikäinen M, Mäenpää T, Viertola J (2002) Color texture classification with color histograms and local binary patterns. In: Proceedings of the 2nd international workshop on texture analysis and synthesis. Denmark, Copenhagen, pp 109–112Google Scholar
  45. 45.
    Qazi IUH, Alata O, Burie JC, Moussa A, Fernandez-Maloigne C (2011) Choice of a pertinent color space for color texture characterization using parametric spectral analysis. Pattern Recognit 44(1):16–31MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Laboratoire LISIC-EA 4491, Université du Littoral Côte d’OpaleMaison de la Recherche Blaise PascalCalais CedexFrance
  2. 2.Laboratoire LAGIS-UMR CNRS 8219Université Lille 1-Sciences et Technologies-Cité ScientifiqueVilleneuve d’AscqFrance

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