Two New Scale-Adapted Texture Descriptors for Image Segmentation

  • Miguel Angel Lozano
  • Francisco Escolano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In texture segmentation it is key to develop descriptors which provide acceptable results without a significant increment of their temporal complexity. In this contribution, we propose two probabilistic texture descriptors: polarity and texture contrast. These descriptors are related to the entropy of both the local distributions of gradient orientation and magnitude. As such descriptors are scale-dependent, we propose a simple method for selecting the optimal scale. Using the features at their optimal scale, we test the performance of these measures with an adaptive version of the ACM clustering method, in which adaptation relies on the Kolmogorov-Smirnov test. Our results with only these two descriptors are very promising.

Keywords

Image Segmentation Optimal Scale Wavelet Frame Texture Segmentation Gradient Orientation 
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.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using Expectation-Maximization and its application to image querying. IEEE Trans. on Pattern Analysis and Machine Intelligence (2002)Google Scholar
  2. 2.
    Gårding, J., Lindeberg, T.: Direct Computation of Shape Cues Using Scale- Adapted Spatial Derivative Operators. International Journal of Computer Vision 17(2), 163–191 (1996)CrossRefGoogle Scholar
  3. 3.
    Hofmann, T., Puzicha, J.: Statistical Models for Co-occurrence Data. MIT AIMemo 1625 Cambridge, MA (1998)Google Scholar
  4. 4.
    Lozano, M.A., Escolano, F.: Recognizing Indoor Images with Unsupervised Segmentation and Graph Matching. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 933–942. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Puzicha, J.: Histogram Clustering for Unsupervised Segmentation and Image Retrieval. Pattern Recognition Letters 20, 899–909 (1999)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Farrokhina, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 23, 1167–1186 (1991)CrossRefGoogle Scholar
  7. 7.
    Knutsson, H., Granlund, G.: Texture analysis using two-dimensional quadrature filters. In: IEEE Computer Society Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pp. 206–213 (1983)Google Scholar
  8. 8.
    Gotlieb, C.C., Kreyszig, H.E.: Texture descriptors based on cooccurrence matrices. Comp. Vision, Graph. and Image Proc. 51, 70–86 (1990)CrossRefGoogle Scholar
  9. 9.
    Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Trans. Image Processing 4(11), 1549–1560 (1995)CrossRefGoogle Scholar
  10. 10.
    Sochen, N., Kimmel, R., Malladi, R.: A General Framework for Low Level Vision. IEEE Trans on Image Processing 7(3), 310–318 (1998)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Miguel Angel Lozano
    • 1
  • Francisco Escolano
    • 1
  1. 1.Robot Vision Group, Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteSpain

Personalised recommendations