As mentioned in many papers, a lot of key parameters of image segmentation algorithms are manually tuned by de- signers. 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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    Title :

    A Learning Approach for Adaptive Image Segmentation


    Contributors:
    Martin, V. (author) / Thonnat, M. (author) / Maillot, N. (author)


    Publication date :

    2006-01-01


    Size :

    697606 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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