Multiscale Combinatorial Grouping | RDL Research Database

Abstract

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.

Keywords

Computer sciencePascal (unit)SegmentationArtificial intelligenceImage segmentationPattern recognition (psychology)Object (grammar)

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Publication Info

Year: 2014
Type: article
Citations: 1202
Access: Closed

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APA Style

                            
                                    Pablo Arbeláez, 
                                
                                    Jordi Pont-Tuset, 
                                
                                    Jon Barron
                                
                                et al.
                            
                            (2014). 
                            Multiscale Combinatorial Grouping. 
                            
                            .
                            https://doi.org/10.1109/cvpr.2014.49

Identifiers

DOI: 10.1109/cvpr.2014.49