Multiscale combinatorial grouping

We propose a unified approach for bottom-up hierarchi-cal image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normal-ized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multi-scale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinato-rial 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, hi-erarchical regions and object candidates. 1