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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)

Abstract

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.

Keywords

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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