Texture Segmentation Using SOM and Multi-scale Bayesian Estimation

  • Tae Hyung Kim
  • Il Kyu Eom
  • Yoo Shin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


This paper presents a likelihood estimation method from SOM (self organizing feature map), and texture segmentation is performed by using Bayesian estimation and SOM. Multi-scale wavelet coefficients are used as input for SOM, and likelihood probabilities for observations are obtained from trained SOMs. Texture segmentation is performed by the likelihood probability from trained SOMs and ML (maximum likelihood) classification. The result of texture segmentation is improved using contextual information. The proposed segmentation method performed better than segmentation method using HMT (hidden Markov trees) model. In addition, texture segmentation results by SOM and multi-scale Bayesian image segmentation technique called HMTseg also performed better than those by HMT and HMTseg.


Coarse Scale Texture Segmentation Training Vector Likelihood Probability Competitive Layer 
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

  • Tae Hyung Kim
    • 1
  • Il Kyu Eom
    • 1
  • Yoo Shin Kim
    • 2
  1. 1.Dept. Electronics EngineeringPusan National UniversityBusanRepublic of Korea
  2. 2.Research Institute of Computer, Information and CommunicationPusan National UnivesityBusanRepublic of Korea

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