Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cluster that contains its feature vector. A major limitation of this approach is that feature space clusters generally lack spatial coherence (i.e., they do not correspond to a compact grouping of pixels). In this paper, we propose a segmentation algorithm that operates simultaneously in feature space and in image space. We define an energy function over both a set of clusters and a labeling of pixels with clusters. In our framework, a pixel is labeled with a single cluster (rather than, for example, a distribution over clusters). Our energy function penalizes clusters that are a poor fit to the data in feature space, and also penalizes clusters whose pixels lack spatial coherence. The energy function can be efficiently minimized using graph cuts. Our algorithm can incorporate both parametric and non-parametric clustering methods. It can be applied to many optimization-based clustering methods, including k-means and k-medians, and can handle models, which are very close in feature space. Preliminary results are presented on segmenting real and synthetic images, using both parametric and non-parametric clustering.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spatially coherent clustering using graph cuts


    Contributors:
    Zabih, R. (author) / Kolmogorov, V. (author)


    Publication date :

    2004-01-01


    Size :

    556996 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Spatially Coherent Clustering Using Graph Cuts

    Zabih, R. / Kolmogorov, V. / IEEE Computer Society | British Library Conference Proceedings | 2004


    Segment-based stereo matching using graph cuts

    Li Hong, / Chen, G. | IEEE | 2004


    Segment-Based Stereo Matching Using Graph Cuts

    Hong, L. / Chen, G. / IEEE Computer Society | British Library Conference Proceedings | 2004


    Fast image blending using watersheds and graph cuts

    Gracias, N. / Mahoor, M. / Negahdaripour, S. et al. | British Library Online Contents | 2009


    Object segmentation using graph cuts based active contours

    Xu, N. / Ahuja, N. / Bansal, R. | British Library Online Contents | 2007