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Unsupervised Hierarchical Weighted Multi-segmenter

  • Michal Haindl
  • Stanislav Mikeš
  • Pavel Pudil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

An unsupervised multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by four causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods.

Keywords

Gaussian Mixture Model Segmentation Result Texture Segmentation Kullback Leibler Divergence Markov Chain Monte Carlo Estimation 
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 2009

Authors and Affiliations

  • Michal Haindl
    • 1
    • 2
  • Stanislav Mikeš
    • 1
  • Pavel Pudil
    • 1
    • 2
  1. 1.Institute of Information Theory and AutomationAcademy of Sciences CRPragueCzech Republic
  2. 2.Faculty of ManagementUniversity of EconomicsHradecCzech Republic

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