We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the expected appearance and position, in an object centered coordinate frame, of features detected by a low-level interest operator. Each object category then has its own distribution over these parts, which are shared between objects. We learn the parameters of this model via a Gibbs sampler which uses the graphical model's structure to analytically average over many parameters. Applied to a database of images of isolated objects, the sharing of parts among objects improves detection accuracy when few training examples are available. We also extend this hierarchical framework to scenes containing multiple objects.


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    Title :

    Learning hierarchical models of scenes, objects, and parts


    Contributors:


    Publication date :

    2005-01-01


    Size :

    1954268 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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