We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal.


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    Title :

    Using appearance and context for outdoor scene object classification


    Contributors:
    Bosch, A. (author) / Munoz, X. (author) / Marti, J. (author)


    Publication date :

    2005-01-01


    Size :

    197773 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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