Research Article Open Access

Using Both a Probabilistic Evolutionary Graph and the Evidence Theory for Color Scene Analysis

Nassim Ammour, Abderrezak Guessoum and Daoud Berkani

Abstract

In this research, we introduce a new color images segmentation algorithm. The color scene analytic method is based on the progress of a probabilistic evolutionary graph. The strategy consists in making grow an evolutionary graph, which presents the scene elements in an unsupervised segmented image. The graph evolution development is based on the computation of the belonging probabilities to the existing classes of the last built region. The space composition matrix of the areas in each class is then given. A space delimitation map of the regions is established by a new method of contour localization and refinement. At last, the final segmented image is established by classification of the pixels in the conflict region using the Dempster-Shafer evidence theory. The effectiveness of the method is demonstrated on real images.

American Journal of Applied Sciences
Volume 5 No. 12, 2008, 1635-1641

DOI: https://doi.org/10.3844/ajassp.2008.1635.1641

Submitted On: 12 December 2007 Published On: 31 December 2008

How to Cite: Ammour, N., Guessoum, A. & Berkani, D. (2008). Using Both a Probabilistic Evolutionary Graph and the Evidence Theory for Color Scene Analysis . American Journal of Applied Sciences, 5(12), 1635-1641. https://doi.org/10.3844/ajassp.2008.1635.1641

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Keywords

  • Evolutionary graph
  • transition
  • node
  • class
  • color
  • region
  • Demspter-Shafer evidence theory