We present a novel approach to image segmentation using iterated Graph Cuts based on multi-scale smoothing. We compute the prior probability obtained by the likelihood from a color histogram and a distance transform using the segmentation results from graph cuts in the previous process, and set the probability as the t-link of the graph for the next process. The proposed method can segment the regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with Gaussian smoothing using different values for the standard deviation. We demonstrate that we can obtain 4.7% better segmentation than that with the conventional approach.


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    Title :

    Image Segmentation Using Iterated Graph Cuts Based on Multi-scale Smoothing


    Contributors:

    Conference:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Published in:

    Computer Vision – ACCV 2007 ; Chapter : 79 ; 806-816


    Publication date :

    2007-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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