Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

作者：Jordi Pont-Tuset, Pablo Arbeláez, Jonathan T. Barron, Ferran Marqués, Jitendra Malik · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2016 · DOI：10.1109/tpami.2016.2537320 · 被引用次数：615 · 研究领域：Advanced Image and Video Retrieval Techniques、Medical Image Segmentation Techniques、Advanced Neural Network Applications

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.