Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation

  • Authors:

  • S. Derivaux;G. Forestier;C. Wemmert;S. Lefèvre

  • Affiliations:

  • Image Sciences, Computer Sciences and Remote Sensing Laboratory, LSIIT UMR 7005, CNRS-University of Strasbourg, Pôle API, Blvd Sébastien Brant, P.O. Box 10413, 67412 Illkirch Cedex, Fran ...;Image Sciences, Computer Sciences and Remote Sensing Laboratory, LSIIT UMR 7005, CNRS-University of Strasbourg, Pôle API, Blvd Sébastien Brant, P.O. Box 10413, 67412 Illkirch Cedex, Fran ...;Image Sciences, Computer Sciences and Remote Sensing Laboratory, LSIIT UMR 7005, CNRS-University of Strasbourg, Pôle API, Blvd Sébastien Brant, P.O. Box 10413, 67412 Illkirch Cedex, Fran ...;Image Sciences, Computer Sciences and Remote Sensing Laboratory, LSIIT UMR 7005, CNRS-University of Strasbourg, Pôle API, Blvd Sébastien Brant, P.O. Box 10413, 67412 Illkirch Cedex, Fran ...

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2010

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Abstract

Automatic image interpretation is often achieved by first performing a segmentation of the image (i.e., gathering neighbouring pixels into homogeneous regions) and then applying a supervised region-based classification. In such a process, the quality of the segmentation step is of great importance in the final classified result. Nevertheless, whereas the classification step takes advantage from some prior knowledge such as learning sample pixels, the segmentation step rarely does. In this paper, we propose to involve such samples through machine learning procedures to improve the segmentation process. More precisely, we consider the watershed transform segmentation algorithm, and rely on both a fuzzy supervised classification procedure and a genetic algorithm in order to respectively build the elevation map used in the watershed paradigm and tune segmentation parameters. We also propose new criteria for segmentation evaluation based on learning samples. We have evaluated our method on remotely sensed images. The results assert the relevance of machine learning as a way to introduce knowledge within the watershed segmentation process.