Color-Based Classification of Natural Rock Images Using Classifier Combinations

  • Leena Lepistö
  • Iivari Kunttu
  • Ari Visa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


Color is an essential feature that describes the image content and therefore colors occurring in the images should be effectively characterized in image classification. The selection of the number of the quantization levels is an important matter in the color description. On the other hand, when color representations using different quantization levels are combined, more accurate multilevel color description can be achieved. In this paper, we present a novel approach to multilevel color description of natural rock images. The description is obtained by combining separate base classifiers that use image histograms at different quantization levels as their inputs. The base classifiers are combined using classification probability vector (CPV) method that has proved to be an accurate way of combining classifiers in image classification.


  1. 1.
    Alkoot, F.M., Kittler, J.: Experimental evaluation of expert fusion strategies. Pattern Recognition Letters 20, 1361–1369 (1999)CrossRefGoogle Scholar
  2. 2.
    Barandela, R., Sánchez, J.S., Valdovinos, R.M.: New applications of ensembles of classifiers. Pattern Analysis & Applications 6, 245–256 (2003)CrossRefGoogle Scholar
  3. 3.
    Brunelli, R., Falavigna, D.: Person Identification Using Multiple Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 955–966 (1995)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, New York (2001)zbMATHGoogle Scholar
  5. 5.
    Duin, R.P.W.: The Combining Classifier: to Train or Not to Train. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 765–770 (2002)Google Scholar
  6. 6.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison Wesley, Reading (1993)Google Scholar
  7. 7.
    Jain, A.K., Prabhakar, S., Chen, S.: Combining Multiple Matchers for a High Security Fingerprint Verification System. Pattern Recognition Letters 20, 1371–1379 (1999)CrossRefGoogle Scholar
  8. 8.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)CrossRefGoogle Scholar
  9. 9.
    Kuncheva, L.I.: A Theoretical Study on Six Classifier Fusion Strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 281–286 (2002)CrossRefGoogle Scholar
  10. 10.
    Lam, L., Suen, C.Y.: Application of majority voting to pattern recognition: An analysis of the behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics 27, 553–567 (1997)CrossRefGoogle Scholar
  11. 11.
    Lepistö, L., Kunttu, I., Autio, J., Visa, A.: Classification of Non-homogenous Textures by Combining Classifiers. In: Proceedings of IEEE International Conference on Image Processing, vol. 1, pp. 981–984 (2003)Google Scholar
  12. 12.
    Lepistö, L., Kunttu, I., Autio, J., Visa, A.: Classification Method for Colored Natural Textures Using Gabor Filtering. In: Proceedings of 12th International Conference on Image Analysis and Processing, pp. 397–401 (2003)Google Scholar
  13. 13.
    Lepistö, L., Kunttu, I., Autio, J., Visa, A.: Combining Classifiers in Rock Image Classification – Supervised and Unsupervised Approach. In: Proceedings of Advanced Concepts for Intelligent Vision Systems, pp. 17–22 (2004)Google Scholar
  14. 14.
    Lin, X., Yacoub, S., Burns, J., Simske, S.: Performance analysis of pattern classifier combination by plurality voting. Pattern Recognition Letters 24, 1959–1969 (2003)CrossRefGoogle Scholar
  15. 15.
    Lu, X., Wang, Y., Jain, A.K.: Combining Classifiers for Face Recognition. In: Proceedings of International Conference on Multimedia and Expo, vol. 3, pp. 13–16 (2003)Google Scholar
  16. 16.
    Wyszecki, G., Stiles, W.S.: Color Science, Concepts and Methods, Quantitative Data and Formulae, 2nd edn. John Wiley & Sons, Chichester (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Leena Lepistö
    • 1
  • Iivari Kunttu
    • 1
  • Ari Visa
    • 1
  1. 1.Institute of Signal ProcessingTampere University of TechnologyTampereFinland

Personalised recommendations