Multiscale Combinatorial Grouping

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...

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Bibliographic Details
Published in:2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 328 - 335
Main Authors: Arbelaez, Pablo, Pont-Tuset, Jordi, Barron, Jon, Marques, Ferran, Malik, Jitendra
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2014
Subjects:
Accuracy
Combinatorial analysis
Computer vision
Conferences
Detectors
Image color analysis
Image resolution
Image segmentation
Measurement
Object Candidates
Pascal (programming language)
Pattern recognition
Segmentation
State of the art
Strategy
Tin
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.49