Ranking list preservation for feature matching
作者：
Highlights：
• We present a very simple but very effective robust feature matching algorithm.
• We are the first to utilize the ranking list to represent the local neighborhood structure.
• The top K ranking distance is used to calculate the local neighborhood similarity.
• A closed-form solution of the objective function is deduced.
• It is robust to a large proportion of outliers, and achieves a good precision/recall balance.
摘要
•We present a very simple but very effective robust feature matching algorithm.•We are the first to utilize the ranking list to represent the local neighborhood structure.•The top K ranking distance is used to calculate the local neighborhood similarity.•A closed-form solution of the objective function is deduced.•It is robust to a large proportion of outliers, and achieves a good precision/recall balance.
论文关键词：Feature matching,Mismatch removal,Top K rank similarity,Local neighborhood structure
论文评审过程：Received 26 May 2019, Revised 5 May 2020, Accepted 18 September 2020, Available online 19 September 2020, Version of Record 24 September 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107665
