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Unsupervised Color-Texture Segmentation

  • Yuzhong Wang
  • Jie Yang
  • Yue Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

An improved approach for JSEG is presented for unsupervised color image segmentation. Instead of color quantization, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling of image data set for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on GMM overcomes the limitations of JSEG successfully and is more robust.

Keywords

Image Segmentation Gaussian Mixture Modeling Segmentation Result Quantization Parameter Synthetic Image 
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 2004

Authors and Affiliations

  • Yuzhong Wang
    • 1
  • Jie Yang
    • 1
  • Yue Zhou
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiaotong UniversityShanghaiP.R. China

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