Image set classification by symmetric positive semi-definite matrices

Masoud Faraki, Mehrtash T. Harandi, Fatih Porikli

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

22 Citations (Scopus)


Representing images and videos by covariance descriptors and leveraging the inherent manifold structure of Symmetric Positive Definite (SPD) matrices leads to enhanced performances in various visual recognition tasks. However, when covariance descriptors are used to represent image sets, the result is often rank-deficient. Thus, most existing approaches adhere to blind perturbation with predefined regularizers just to be able to employ inference tools. To overcome this problem, we introduce novel similarity measures specifically designed for rank-deficient covariance descriptors, i.e., symmetric positive semi-definite matrices. In particular, we derive positive definite kernels that can be decomposed into the kernels on the cone of SPD matrices and kernels on the Grassmann manifolds. Our experiments evidence that, our method achieves superior results for image set classification on various recognition tasks including hand gesture classification, face recognition from video sequences, and dynamic scene categorization.

Original languageEnglish
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016)
EditorsGreg Mori, Robert Pless, Scott McCloskey, Rahul Sukthankar
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509006410
ISBN (Print)9781509006427
Publication statusPublished - 23 May 2016
Externally publishedYes
EventIEEE Winter Conference on Applications of Computer Vision 2016 - Lake Placid, United States of America
Duration: 7 Mar 201610 Mar 2016 (Proceedings)


ConferenceIEEE Winter Conference on Applications of Computer Vision 2016
Abbreviated titleWACV 2016
Country/TerritoryUnited States of America
CityLake Placid
Internet address

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