We present a quantitative evaluation of SE-MinCut, a novel segmentation algorithm based on spectral embedding and minimum cut. We use human segmentations from the Berkeley segmentation database as ground truth and propose suitable measures to evaluate segmentation quality. With these measures we generate precision/recall curves for SE-MinCut and three of the leading segmentation algorithms: mean-shift, normalized Cuts, and the local variation algorithm. These curves characterize the performance of each algorithm over a range of input parameters. We compare the precision/recall curves for the four algorithms and show segmented images that support the conclusions obtained from the quantitative evaluation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Quantitative evaluation of a novel image segmentation algorithm


    Contributors:


    Publication date :

    2005-01-01


    Size :

    932932 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Quantitative Performance Evaluation Algorithms for Pavement Distress Segmentation

    Kaul, Vivek / Tsai, Yichang (James) / Mersereau, Russell M. | Transportation Research Record | 2010



    A Genetic Algorithm for Image Segmentation

    Bosco, G. L. / IEEE | British Library Conference Proceedings | 2001