IEEE Trans Pattern Anal Mach Intell. 2017 Jan;39(1):128-140. doi: 10.1109/TPAMI.2016.2537320. Epub 2016 Mar 2.
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.
我们提出了一种用于自下而上的分层图像分割和对象提议生成的统一方法，称为多尺度组合分组（MCG）。为此，我们首先开发了一种快速归一化切割算法。然后，我们提出了一种高性能的分层分割器，该分割器有效利用了多尺度信息。最后，我们提出了一种分组策略，通过有效地探索其组合空间，将我们的多尺度区域组合成高度精确的对象提议。我们还提出了单尺度组合分组（SCG），这是 MCG 的一个更快版本，它可以在不到五秒的时间内为每张图像生成有竞争力的提议。我们在 BSDS500、SegVOC12、SBD 和 COCO 数据集上进行了广泛而全面的实证验证，结果表明 MCG 生成了最先进的轮廓、分层区域和对象提议。
