  We propose a unified approach for bottom-up hierarchical image segmentation
and object proposal 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
proposals by exploring efficiently their combinatorial space. We also present
Single-scale Combinatorial Grouping (SCG), a faster version of MCG that
produces competitive proposals in under five second per image. We conduct an
extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD,
and COCO datasets, showing that MCG produces state-of-the-art contours,
hierarchical regions, and object proposals.
