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A Learning Approach for Adaptive Image Segmentation

Vincent Martin 1, * Nicolas Maillot 1 Monique Thonnat 1 
* Corresponding author
Abstract : As mentioned in many papers, a lot of key parameters of image segmentation algorithms are manually tuned by designers. This induces a lack of flexibility of the segmentation step in many vision systems. By a dynamic control of these parameters, results of this crucial step could be drastically improved. We propose a scheme to automatically select segmentation algorithm and tune theirs key parameters thanks to a preliminary supervised learning stage. This paper details this learning approach which is composed by three steps: (1) optimal parameters extraction, (2) algorithm selection learning, and (3) generalization of parametrization learning. The major contribution is twofold: segmentation is adapted to the image to segment, and in the same time, this scheme can be used as a generic framework, independant of any application domain.
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Vincent Martin, Nicolas Maillot, Monique Thonnat. A Learning Approach for Adaptive Image Segmentation. International Conference on Computer Vision Systems, Jan 2006, New York City, NJ, United States. pp.40, ⟨10.1109/ICVS.2006.4⟩. ⟨inria-00499629⟩



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