Advertisement

Object Segmentation

Part of the The Information Retrieval Series book series (INRE, volume 9)

6.3.3 Summary

We have highlighted some experiment results on home photo segmentation. The MRF/GD model is proved to be useful in practice. Experiment results demonstrate that the CIE— L*u*v* color space performs better than HSV in the unsupervised clustering based segmentation on digital home photos. The region merging algorithm can serve as an effective compensation step for the clustering based segmentation. Texture features extracted by the wavelet frame transform are found to be promising in discriminating different textures. The combination of color and texture for the segmentation of natural images is a challenging research area.

Keywords

Texture Feature Markov Random Field Region Type Wavelet Frame Cluster Validation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Y. Lu, T. Chen, “An Efficient Color CLustering Algorithm for Image Retrieving”, IFMIP, 1998Google Scholar
  2. 2.
    K. Takahashi et al, “Color Image Segmentation Using ISODATA Clustering Method”, Second Asian Conference on Computer Vision, Vol. 1, pp. 523–527, 1995Google Scholar
  3. 3.
    D. langan, J. Modestino, J. Zhang, “Cluster Validation for Unsupervised Stochastic Model-Based Image Segmentation”, IEEE Trans on Image processing, Vol. 7, No. 2, pp. 180–194, 1998CrossRefGoogle Scholar
  4. 4.
    S. Geman, D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”, IEEE Trans on PAMI, Vol. 6, No. 6, pp. 721–741, 1984Google Scholar
  5. 5.
    D. langan, J. Modestino, J. Zhang, “Multiresolution Color Image Segmentation”, IEEE Trans on PAMI, Vol. 16, No. 7, pp. 689–700, 1994Google Scholar
  6. 6.
    T. Pappas, “An Adaptive Clustering Algorithm for Image Segmentation”, IEEE Trans on PAMI, 1992Google Scholar
  7. 7.
    H. Derin, H. Elliott, “Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields”, IEEE Trans on PAMI, Vol. 9, No. 1, pp. 39–55, 1987Google Scholar
  8. 8.
    J. Rissanen, “Modeling by Shortest Data Description”, Automatica, Vol. 14, pp. 465–471, 1978CrossRefzbMATHGoogle Scholar
  9. 9.
    J. Buhmann, H. Kuhnel, “Vector Quantization with Complexity Costs”, IEEE Trans on IT, Vol. 39, pp. 1133–1145, 1993Google Scholar
  10. 10.
    Y. Linde, A. Buzo, R. Gray, “An Algorithm for Vector Quantizer Design”, IEEE Trans on Comm, Vol. 28, No. 1, pp. 84–95, 1980Google Scholar
  11. 11.
    J. Zhang, J. Modestino, “A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-based Image Segmentation”, IEEE Trans on PAMI, Vol. 12, pp. 1009–1017, 1990Google Scholar
  12. 12.
    P. Saint-Marc et al, “Adaptive Smoothing: A General Tool for Early Vision”, IEEE Trans on PAMI, Vol. 13, No. 6, pp. 514–529, 1991Google Scholar
  13. 13.
    M. Levenson, “Adaptive Smoothing of Images with Local Weighted Regression”, SPIE, Vol. 2823, pp. 85–99, 1996Google Scholar
  14. 14.
    R. Carmona and S. Zhong, “Adaptive Smoothing Respecting Feature Directions”, IEEE Trans on Image Processing, Vol. 7, No. 3, pp. 353–358, 1998CrossRefGoogle Scholar
  15. 15.
    G. Milligan, M. Cooper, “An Examination of Procedures for Determining the Number of Clusters in a Data Set”, Psychometrika, Vol. 50, pp. 159–179, 1985Google Scholar
  16. 16.
    H. Akaike, “A New Look at the Statistical Model Identification”, IEEE Trans. on Automatic Control, Vol. 19, pp. 716–722, 1974zbMATHMathSciNetGoogle Scholar
  17. 17.
    J. Bigun et al, “N-folded Symmetries by Complex Moments in Gabor Space and Their Application to Unsupervised Image Segmentation”, IEEE Trans on PAMI, Vol. 16, pp. 80–87, 1994Google Scholar
  18. 18.
    J. R. Bach et al, “The Virage Image Search Engine: An Open Framework for Image Management”, Virage, Inc., San DiegoGoogle Scholar
  19. 19.
    A. Gupta, “Visual Information Retrieval Technology: A Virage Perspective”, Virage, Inc., San DiegoGoogle Scholar
  20. 20.
    Sing-Tze Bow, “Pattern Recognition and Image Preprocessing”, Marcel Dekker Inc., New York, 1992Google Scholar
  21. 21.
    Jiankang Wu, Digital Image Analysis, China Communication Press, Beijing, 1989Google Scholar
  22. 22.
    T. Pavlidos and Y. T. Liow, “Integrating Region Growing and Edge Detection, IEEE Trans on PAMI, Vol. 12, No. 3, pp. 225–233, 1990Google Scholar
  23. 23.
    H. Levkowitz, “Color Theory and Modeling for Computer Graphics, Visualization, and Multimedia Applications”, Kluwer Academic Publishers, 1997Google Scholar
  24. 24.
    J. R. Smith, “Integrated Spatial and Feature Image Systems: retrieval, Analysis and Compression”, Columbia University, 1997Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Personalised recommendations