Evolutionary image segmentation

  • Primo Zingaretti
  • Antonella Carbonaro
  • Paolo Puliti
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

We describe an approach to image segmentation based on a two-layer module that is executed until a good segmentation is achieved, providing an evolution of previous segmentation results at each execution. The first layer performs a global segmentation of an image of decreasing area at each evolution by adopting a genetic algorithm learning technique to select segmentation parameters that give better results. The second layer provides the input to the next evolution by selecting the segmented regions that need further optimisation. A main goal of our system is to perform the segmentation without using neither ground-truth information nor human judgement. Thus, edge detection is performed to assess the performance of region segmentation and to guide the evolution of segmentation. Experimental results are consistent with what is observed visually.

Keywords

Image Segmentation Segmentation Result Global Fitness Good Segmentation Image Segmentation Technique 
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 1997

Authors and Affiliations

  • Primo Zingaretti
    • 1
  • Antonella Carbonaro
    • 1
  • Paolo Puliti
    • 1
  1. 1.Istituto di Informatica, Facoltá di IngegneriaUniversitá di AnconaAnconaItaly

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