CPMC: Constrained Parametric Min-Cuts for Automatic Object Segmentation
Joao Carreira and Cristian Sminchisescu

This is the implementation of CPMC used in our VOC2010 segmentation challenge submissions. It is an extention of the algorithm used in our VOC2009 submission, which is described in Constrained Parametric Min-Cuts for Automatic Object Segmentation, CVPR 2010 [1]. Changes over [1] are in reducing computation time and the usage of subframes to better segment small objects. The learning code is not yet provided, but the package includes two pretrained ranking models.

The system has been tested on MATLAB 7.9 and 7.10 on Linux 32bits and 64bits machines with 10 Gb of memory.

For details see the README file in the package below.

Reference

[54] Constrained Parametric Min-Cuts for Automatic Object Segmentation
J. Carreira and C. Sminchisescu

IEEE International Conference on Computer Vision and Pattern Recognition, 2010

Related References

[65] Image Segmentation by Figure-Ground Composition into Maximal Cliques
A. Ion and J. Carreira and C. Sminchisescu

IEEE International Conference on Computer Vision, 2011

[55] Image Segmentation by Discounted Cumulative Ranking on Maximal Cliques
J. Carreira, A. Ion, and C. Sminchisescu

Technical Report 06-2010 (arXiv:1009.4823), Computer Vision and Machine Learning Group, Institute for Numerical Simulation, University of Bonn, 2010

[53] Object Recognition as Ranking Holistic Figure-Ground Hypotheses
F. Li and J. Carreira and C. Sminchisescu

IEEE International Conference on Computer Vision and Pattern Recognition, 2010

Code