This paper presents an iterative maximum likelihood framework for motion segmentation. Our representation of the segmentation problem is based on a similarity matrix for the motion vectors for pairs of pixel blocks. By applying eigendecomposition to the similarity matrix, we develop a maximum likelihood method for grouping the pixel blocks into objects which share a common motion vector. We experiment with the resulting clustering method on a number of real-world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%.


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    Title :

    Maximum likelihood motion segmentation using eigendecomposition


    Contributors:


    Publication date :

    2001-01-01


    Size :

    645813 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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