Multi-scale context for scene labeling via flexible segmentation graph

作者：

Highlights：

• We propose a novel flexible segmentation graph (FSG) representation to capture multi-scale visual context for scene labeling problem.

• In the scenario of FSG, we establish a contextual fusion model to formulate multi-scale context.

• We have evaluated our method on four datasets. Our model yields similar to or better labeling result than competing models, and is also computationally efficient.

摘要

Highlights•We propose a novel flexible segmentation graph (FSG) representation to capture multi-scale visual context for scene labeling problem.•In the scenario of FSG, we establish a contextual fusion model to formulate multi-scale context.•We have evaluated our method on four datasets. Our model yields similar to or better labeling result than competing models, and is also computationally efficient.

论文关键词：Scene labeling,Semantic segmentation,Multi-scale context,Flexible segmentation graph,Feature extraction,Classification

论文评审过程：Received 31 July 2015, Revised 12 March 2016, Accepted 17 March 2016, Available online 25 March 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.03.023