Agglomerative oversegmentation using dual similarity and entropy rate

作者：

Highlights：

• The oversegmentation results of our proposed AOS take the local texture into consideration and maintain the local geometric structures of the target objects in images.

• A fast agglomerative algorithm is proposed using the complete linkage.

• The gain of the entropy rate of random walks utilized in this study not to be updated, which reduces the computational cost.

• A novel dual similarity is proposed to dig more information out of the area with lower intensity contrast in an image.

摘要

•The oversegmentation results of our proposed AOS take the local texture into consideration and maintain the local geometric structures of the target objects in images.•A fast agglomerative algorithm is proposed using the complete linkage.•The gain of the entropy rate of random walks utilized in this study not to be updated, which reduces the computational cost.•A novel dual similarity is proposed to dig more information out of the area with lower intensity contrast in an image.

论文关键词：Oversegmentation,Agglomerative algorithm,Entropy rate,Remote sensing

论文评审过程：Received 27 September 2018, Revised 24 March 2019, Accepted 1 May 2019, Available online 2 May 2019, Version of Record 6 May 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.010