In this work, we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. We propose a novel framework to address this problem that is based on the combination of the expectation maximization (EM) algorithm and generative models for object categories. Using a concise formulation of the interaction between these two processes, segmentation is interpreted as the E step, assigning observations to models, whereas object detection/analysis is modelled as the M-step, fitting models to observations. We present in detail the segmentation and detection processes comprising the E and M steps and demonstrate results on the joint detection and segmentation of the object categories of faces and cars.


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    Title :

    An expectation maximization approach to the synergy between image segmentation and object categorization


    Contributors:
    Kokkinos, I. (author) / Maragos, P. (author)


    Publication date :

    2005-01-01


    Size :

    584933 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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