PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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 exploring efficiently 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.
CitationArbelaez, P. [et al.]. Multiscale combinatorial grouping. A: IEEE Conference on Computer Vision and Pattern Recognition. "2014 IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, Ohio: proceedings". Columbus, Ohio: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 328-335.
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