Good recognition is non-metric
作者：
Highlights：
• A thorough and critical review of the most recent literature in metric learning and related fields.
• A new general definition of recognition that is not restricted to pair matching.
• An extensive meta-analysis of metric learning on Caltech 101 and Labeled Faces in the Wild.
• An experimental evaluation of top performing metric and non-metric algorithms.
• A series of useful recommendations, based on our results, for recognition algorithm designs.
摘要
Author-Highlights•A thorough and critical review of the most recent literature in metric learning and related fields.•A new general definition of recognition that is not restricted to pair matching.•An extensive meta-analysis of metric learning on Caltech 101 and Labeled Faces in the Wild.•An experimental evaluation of top performing metric and non-metric algorithms.•A series of useful recommendations, based on our results, for recognition algorithm designs.
论文关键词：Machine learning,Metric learning,Recognition,Computer vision,Face recognition,Object recognition
论文评审过程：Received 11 May 2013, Revised 5 December 2013, Accepted 27 February 2014, Available online 11 March 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.02.018
