Advertisement

Color Pair Clustering for Texture Detection

  • Lech Szumilas
  • Allan Hanbury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)

Abstract

A novel approach to the extraction of image regions of uniform color and its application to automatic texture detection is discussed. The method searches for alternating color patterns, through hierarchical clustering of color pairs from adjacent image regions. The final result is a hierarchy of texture regions, described by their boundaries and a set of features, detected at multiple accuracy levels. The results are presented on some images of natural scenes from the Berkeley segmentation dataset and benchmark.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chetverikov, D.: Pattern regularity as a visual key. Image and Vision Computing 18, 975–985 (2000)CrossRefGoogle Scholar
  2. 2.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43, 7–27 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Mičušík, B., Hanbury, A.: Steerable semi-automatic segmentation of textured images. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 35–44. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)Google Scholar
  5. 5.
    Fowlkes, C., Martin, D., Malik, J.: Learning affinity functions for image segmentation: Combining patch-based and gradient-based approaches. In: Proc. CVPR 2003, pp. II: 54–61 (2003)Google Scholar
  6. 6.
    Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. IJCV 46(3), 223–247 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Sumengen, B., Manjunath, B.S., Kenney, C.: Image segmentation using curve evolution and region stability. In: Proc. International Conference on Pattern Recognition, vol. 2 (2002)Google Scholar
  8. 8.
    Soille, P.: Morphological Image Analysis. Springer, Heidelberg (2002)Google Scholar
  9. 9.
    Marcotegui, B., Beucher, S.: Fast implementation of waterfall based on graphs. In: Mathematical Morphology and its Applications to Image Processing. Proc. ISMM 2005, pp. 177–186 (2005)Google Scholar
  10. 10.
    Comanicu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24, 603–619 (2002)CrossRefGoogle Scholar
  11. 11.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: International Conference on Computer Vision, pp. 10–17 (2003)Google Scholar
  12. 12.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  13. 13.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 959–973 (2003)CrossRefGoogle Scholar
  14. 14.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Chichester (2000)Google Scholar
  15. 15.
    Li, G., An, C., Pang, J., Tan, M., Tu, X.: Color image adaptive clustering segmentation. In: Proc. Image and Graphics Conference, pp. 104–107 (2004)Google Scholar
  16. 16.
    Szumilas, L.: Feature co-occurrence texture detector. Technical report, PRIP-TR-099. Pattern Recognition and Image Processing, Vienna University of Technology (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lech Szumilas
    • 1
  • Allan Hanbury
    • 1
  1. 1.Pattern Recognition and Image Processing Group, Institute of Computer Aided AutomationVienna University of TechnologyViennaAustria

Personalised recommendations