On machine learning in watershed segmentation

Abstract : Automatic image interpretation could be achieved by first performing a segmentation of the image, i.e. aggregating similar pixels to form regions, then use a supervised region- based classification. In such a process, the quality of the segmentation step is of great importance. 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 machine learning to improve the segmentation process using the watershed transform. More precisely, we apply a fuzzy supervised classification and a genetic algorithm in order to respectively generate the elevation map used in the watershed transform and tune segmentation parameters. The results from our evolutionary supervised watershed algorithm confirm the relevance of machine learning to introduce knowledge in the watershed segmentation process.
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Communication dans un congrès
IEEE International Workshop on Machine Learning in Signal Processing (MLSP), 2007, Greece. pp.187-192, 2007, <10.1109/MLSP.2007.4414304>
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Sébastien Derivaux, Sébastien Lefèvre, Cédric Wemmert, Jerzy Korczak. On machine learning in watershed segmentation. IEEE International Workshop on Machine Learning in Signal Processing (MLSP), 2007, Greece. pp.187-192, 2007, <10.1109/MLSP.2007.4414304>. <hal-00516076>

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