Statistical adaptive metric learning for action feature set recognition in the wild

Shuanglu Dai, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper proposes a statistical adaptive metric learning method by exploring various selections and combinations of multiple statistics in a unified metric learning framework. Most statistics have certain advantages in specific controlled environments, and systematic selections and combinations can adapt them to more realistic “in the wild” scenarios. In the proposed method, multiple statistics, include means, covariance matrices and Gaussian distributions, are explicitly mapped or generated in the Riemannian manifolds. Subsequently, by embedding the heterogeneous manifolds in their tangent Hilbert space, the deviation of principle elements is analyzed. Hilbert subspaces with minimal principle elements deviation are then selected from multiple statistical manifolds. After that, Mahalanobis metrics are introduced to map the selected subspaces back into the Euclidean space. A uniformed optimization framework is finally performed based on the Euclidean distances. Such a framework enables us to explore different metric combinations. Therefore our final learning becomes more representative and effective than exhaustively learning from all the hybrid metrics. Experiments in both static and dynamic scenarios show that the proposed method performs effectively in the wild scenarios.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings
EditorsMark Elendt, Richard Boyle, Eric Ragan, Bahram Parvin, Rogerio Feris, Tim McGraw, Ioannis Pavlidis, Regis Kopper, George Bebis, Darko Koracin, Zhao Ye, Gunther Weber
Pages657-667
Number of pages11
DOIs
StatePublished - 2015
Event11th International Symposium on Advances in Visual Computing, ISVC 2015 - Las Vegas, United States
Duration: 14 Dec 201516 Dec 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9474
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Symposium on Advances in Visual Computing, ISVC 2015
Country/TerritoryUnited States
CityLas Vegas
Period14/12/1516/12/15

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