Cosegmentation Revisited: Models and Optimization

  • Sara Vicente
  • Vladimir Kolmogorov
  • Carsten Rother
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the “reward” model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4].

In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure.


Color Histogram Appearance Model Foreground Object Subgradient Method Global Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sara Vicente
    • 1
  • Vladimir Kolmogorov
    • 1
  • Carsten Rother
    • 2
  1. 1.University College London 
  2. 2.Microsoft Research Cambridge 

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