Boosting Fisher vector based scoring functions for person re-identification
作者：
Highlights：
• We propose BFiVe, a new supervised algorithm for single-shot person re-identification.
• The descriptors are a set of compressed local Fisher vectors extracted from a coarse to fine image subdivision.
• In the training step each region gives rise to a learnt weak ranking function.
• The ranking function of the image gallery is obtained by a boosted selection of a weak learner subset.
• The matching rate at rank 1 on VIPeR is 38.9%, on 3DPes 41.7%, on PRID-2011 19.6%, and on i-LIDS-119 48.1%.
摘要
•We propose BFiVe, a new supervised algorithm for single-shot person re-identification.•The descriptors are a set of compressed local Fisher vectors extracted from a coarse to fine image subdivision.•In the training step each region gives rise to a learnt weak ranking function.•The ranking function of the image gallery is obtained by a boosted selection of a weak learner subset.•The matching rate at rank 1 on VIPeR is 38.9%, on 3DPes 41.7%, on PRID-2011 19.6%, and on i-LIDS-119 48.1%.
论文关键词：Person re-identification,Fisher vector,Adaptive boosting,Likelihood ratio,Similarity ranking
论文评审过程：Received 30 August 2014, Revised 17 August 2015, Accepted 16 September 2015, Available online 19 October 2015, Version of Record 3 November 2015.
论文官网地址：https://doi.org/10.1016/j.imavis.2015.09.008
