A comprehensive overview of relevant methods of image cosegmentation

作者：

Highlights：

• A new taxonomy of existing cosegmentation methods according to their frameworks.

• For each method, we present adopted features and model, advantages and limitations.

• A numerical comparison of relevant existing cosegmentation methods is proposed.

• The performance of various methods was evaluated on five challenging datasets.

• iCoseg, MSRC, Internet, FlickrMFC and PASCAL-VOC datasets are used for evaluation.

摘要

•A new taxonomy of existing cosegmentation methods according to their frameworks.•For each method, we present adopted features and model, advantages and limitations.•A numerical comparison of relevant existing cosegmentation methods is proposed.•The performance of various methods was evaluated on five challenging datasets.•iCoseg, MSRC, Internet, FlickrMFC and PASCAL-VOC datasets are used for evaluation.

论文关键词：Image cosegmentation,Foreground objects,Markov random fields,Random walker,Co-saliency,Clustering,Deep learning

论文评审过程：Received 12 December 2018, Revised 6 August 2019, Accepted 26 August 2019, Available online 27 August 2019, Version of Record 6 September 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112901