Statistical adaptive metric learning in visual action feature set recognition

作者：

Highlights：

• A statistical adaptive metric learning (SAML) is proposed to classify action features.

• SAML explores multiple statistic combinations for feature sets in different scales.

• Discriminative statistic subspace is learned by a unified metric learning framework.

• High competitive performances are achieved by SAML on five benchmark databases.

摘要

•A statistical adaptive metric learning (SAML) is proposed to classify action features.•SAML explores multiple statistic combinations for feature sets in different scales.•Discriminative statistic subspace is learned by a unified metric learning framework.•High competitive performances are achieved by SAML on five benchmark databases.

论文关键词：Feature set classification,Hybrid statistic modeling,Metric learning,Manifold selection

论文评审过程：Received 22 August 2015, Revised 22 February 2016, Accepted 7 April 2016, Available online 16 April 2016, Version of Record 10 November 2016.

论文官网地址：https://doi.org/10.1016/j.imavis.2016.04.003