Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

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 t...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 39; číslo 1; s. 128 - 140
Hlavní autori: Pont-Tuset, Jordi, Arbelaez, Pablo, Barron, Jonathan T., Marques, Ferran, Malik, Jitendra
Médium: Journal Article Publikácia
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Combinatorial analysis
Detectors
Digital techniques
Image color analysis
Image processing
Image segmentation
Imatges
Indexes
Merging
Normalized cuts
Object proposals
Object recognition
Object tracking
Partitioning algorithms
Processament
Proposals
So, imatge i multimèdia
Tècniques digitals
Àrees temàtiques de la UPC
ISSN:0162-8828, 2160-9292, 1939-3539
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Popis
Shrnutí: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 seconds 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2016.2537320