Unsupervised Image Segmentation Using an Iterative Entropy Regularized Likelihood Learning Algorithm

  • Zhiwu Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


As for unsupervised image segmentation, one important application is content based image retrieval. In this context, the key problem is to automatically determine the number of regions(i.e., clusters) for each image so that we can then perform a query on the region of interest. This paper presents an iterative entropy regularized likelihood (ERL) learning algorithm for cluster analysis based on a mixture model to solve this problem. Several experiments have demonstrated that the iterative ERL learning algorithm can automatically detect the number of regions in a image and outperforms the generalized competitive clustering.


Mixture Model Image Segmentation Color Image Iterative Algorithm Gaussian Mixture Model 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shih, F.Y., Cheng, S.X.: Automatic Seeded Region Growing for Color Image Segmentation. Image and Vision Computing 23, 877–886 (2005)CrossRefGoogle Scholar
  2. 2.
    Dai, M., Baylou, P., Humbert, L., Najim, M.: Image Segmentation by a Dynamic Thresholding Using Edge Detection Based on Cascaded Uniform Filters. Signal Processing 52, 49–63 (1996)MATHCrossRefGoogle Scholar
  3. 3.
    Banerjee, M., Kundu, M.K.: Edge Based Features for Content Based Image Retrieval. Pattern Recognition 36, 2649–2661 (2003)CrossRefGoogle Scholar
  4. 4.
    Render, R.A., Walker, H.F.: Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM Review 26, 195–239 (1984)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Chinrungrueng, C., Sequin, C.H.: Optimal Adaptive K-Means Algorithm with Dynamic Adjustment of Learning Rate. IEEE Transactions on Neural Networks 6, 157–169 (1995)CrossRefGoogle Scholar
  6. 6.
    Dennis, D.C., Finbarr, O.S.: Asymptotic Analysis of Penalized Likelihood and Related Estimators. The Annals of Statistics 18, 1676–1695 (1990)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks 10, 988–999 (1999)CrossRefGoogle Scholar
  8. 8.
    Boujeman, N.: Generalized Competitive Clustering for Image Segmentation. In: 19th International Conference of the North American Fuzzy Information Processing Society, pp. 133–137 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiwu Lu
    • 1
  1. 1.Institute of Computer Science & Technology of Peking UniversityBeijingChina

Personalised recommendations