P. Arbelaez, Pont-Tuset, J., Barron, J., Marqués, F., and Malik, J., Multiscale Combinatorial Grouping, in Computer Vision and Pattern Recognition (CVPR), 2014. (2.8 MB)


We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by efficiently exploring their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object candidates.

 Code and pre-computed results available here.