Human action recognition based on bag of features and multi-view neural networks

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

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

6 Citations (Scopus)
217 Downloads (Pure)

Abstract

In this paper, we employ Single-hidden Layer Feedforward Neural networks in order to perform human action recognition based on multiple action representations. In order to determine both optimized network and action representation combination weights, we propose an optimization process that jointly minimizes the overall network training error and the within-class variance of the training data in the corresponding hidden layer spaces. The proposed approach has been evaluated by using the state-of-the-art Bag of Features-based action video representation on three publicly available action recognition databases, where it outperforms two commonly used video representation combination approaches, as well as the best single-descriptor classification outcome.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP 2014)
Subtitle of host publicationProceedings of a meeting held 27-30 October 2014, Paris, France
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1510-1514
Number of pages5
ISBN (Electronic)9781479957514
ISBN (Print)9781479957521
DOIs
Publication statusPublished - Mar 2015
EventIEEE International Conference on Image Processing (ICIP) - Paris, France
Duration: 27 Oct 201430 Oct 2014

Publication series

NameIEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing (ICIP)
CountryFrance
CityParis
Period27/10/1430/10/14

Keywords

  • Single-hidden Layer Feedforward Neural networks
  • Multi-view Learning
  • Human Action Recognition
  • Bag of Features

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