Merging linear discriminant analysis with Bag of Words model for human action recognition

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

In this paper we propose a novel method for human action recognition, that unifies discriminative Bag of Words (BoW)-based video representation and discriminant subspace learning. An iterative optimization scheme is proposed for sequential discriminant BoWs-based action representation and codebook adaptation based on action discrimination in a reduced dimensionality feature space where action classes are better discriminated. Experiments on four publicly available action recognition data sets demonstrate that the proposed unified approach increases the discriminative ability of the obtained video representation, providing enhanced action classification performance.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publicationProceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages832-836
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983407
DOIs
Publication statusPublished - Jan 2016
Event2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada
Duration: 27 Sep 201530 Sep 2015

Conference

Conference2015 IEEE International Conference on Image Processing (ICIP)
Country/TerritoryCanada
CityQuebec City, ON
Period27/09/1530/09/15

Keywords

  • Bag of Words
  • Discriminant Learning

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