Advertisement

Colour Texture Segmentation by Region-Boundary Cooperation

  • Jordi Freixenet
  • Xavier Muñoz
  • Joan Martí
  • Xavier Lladó
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)

Abstract

A colour texture segmentation method which unifies region and boundary information is presented in this paper. The fusion of several approaches which integrate both information sources allows us to exploit the benefits of each one. We propose a segmentation method which uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation, which allow to estimate the colour behaviour, and classical co-occurrence matrix based texture features. When the region information is defined, accurate boundary information can be extracted. Afterwards, regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. In contrast with other approaches, our method achieves relevant results on images with regions with the same texture and different colour (as well as with regions with the same colour and different texture), demonstrating the performance of our proposal. Furthermore, the method has been quantitatively evaluated and compared on a set of mosaic images, and results on real images are shown and analysed.

Keywords

Image Segmentation Texture Feature Machine Intelligence Kernel Density Estimation Colour Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Pavlidis, T., Liow, Y.: Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 225–233 (1990)CrossRefGoogle Scholar
  2. 2.
    Haralick, R., Shapiro, L.: Image segmentation techniques. Computer Vision, Graphics and Image Processing 29, 100–132 (1985)CrossRefGoogle Scholar
  3. 3.
    Pal, N., Pal, S.: A review on image segmentation techniques. Pattern Recognition 26, 1277–1294 (1993)CrossRefGoogle Scholar
  4. 4.
    Drimbarean, A., Whelan, P.: Experiments in colour texture analysis. Pattern Recognition Letters 22, 1161–1167 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Muñoz, X., Freixenet, J., Cufí, X., Martí, J.: Strategies for image segmentation combining region and boundary information. Pattern Recognition Letters 24, 375–392 (2003)CrossRefGoogle Scholar
  6. 6.
    Muñoz, X., Martí, J., Cufí, X., Freixenet, J.: Unsupervised active regions for multiresolution image segmentation. In: IAPR International Conference on Pattern Recognition, Quebec, Canada (2002)Google Scholar
  7. 7.
    Van de Wouwer, G., Scheunders, P., Livens, S., Van Dyck, D.: Wavelet correlation signatures for color texture characterization. Pattern Recognition 32, 443–451 (1999)CrossRefGoogle Scholar
  8. 8.
    Dubuisson-Jolly, M.P., Gupta, A.: Color and texture fusion: Application to aerial image segmentation and gis updating. Image and Vision Computing 18, 823–832 (2000)CrossRefGoogle Scholar
  9. 9.
    Manduchi, R.: Bayesian fusion of color and texture segmentations. In: International Conference on Computer Vision, Corfu, Greece, vol. 2, pp. 956–962 (1999)Google Scholar
  10. 10.
    Caelli, T., Reye, D.: On the classification of image regions by color, texture and shape. Pattern Recognition 26, 461–470 (1993)CrossRefGoogle Scholar
  11. 11.
    Thai, B., Healey, G.: Modelling and classifying symmetries using a multiscale opponent colour representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1224–1235 (1998)CrossRefGoogle Scholar
  12. 12.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Color and texturebased image segmentation using em and its application to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1026–1038 (2002)CrossRefGoogle Scholar
  13. 13.
    Rui, Y., She, A., Huang, T.: Automated region segmentation using attractionbased grouping in spatial-color-texture space. In: IEEE International Conference on Image Processing, vol. 1, pp. 53–56. Lausanne, Switzerland (1996)Google Scholar
  14. 14.
    Panjwani, D., Healey, G.: Markov random field models for unsupervised segmentation of textured color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 939–954 (1995)CrossRefGoogle Scholar
  15. 15.
    Paschos, G.: Fast color texture recognition using chromacity moments. Pattern Recognition Letters 21, 837–841 (2000)CrossRefGoogle Scholar
  16. 16.
    Mirmehdi, M., Petrou, M.: Segmentation of color textures. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 142–159 (2000)CrossRefGoogle Scholar
  17. 17.
    Tu, Z., Zhu, S.: Image segmentation by data-driven markov chain monte carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 657–673 (2002)CrossRefGoogle Scholar
  18. 18.
    Khotanzad, A., Chen, J.: Unsupervised segmentation of texture images by edge detection in multidimensional features. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 414–421 (1989)CrossRefGoogle Scholar
  19. 19.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 1–18 (2002)CrossRefGoogle Scholar
  20. 20.
    Will, S., Hermes, L., Buhmann, J., Puzicha, J.: On learning texture edge detectors. In: IEEE International Conference on Image Processing, Vancouver, Canada, vol. III, pp. 887–880 (2000)Google Scholar
  21. 21.
    Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision 46, 223–247 (2002)zbMATHCrossRefGoogle Scholar
  22. 22.
    Chakraborty, A., Staib, L., Duncan, J.: Deformable boundary finding influenced by region homogeneity. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, Washington, vol. 94, pp. 624–627 (1994)Google Scholar
  23. 23.
    Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multi-band image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 884–900 (1996)CrossRefGoogle Scholar
  24. 24.
    Haralick, R., Shanmugan, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3, 610–621 (1973)CrossRefGoogle Scholar
  25. 25.
    Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. In: IEEE International Conference on Image Processing, Washington, DC, vol. III, pp. 53–56 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jordi Freixenet
    • 1
  • Xavier Muñoz
    • 1
  • Joan Martí
    • 1
  • Xavier Lladó
    • 1
  1. 1.Computer Vision and Robotics GroupUniversity of GironaGironaSpain

Personalised recommendations