Exploiting subclass information in one-class support vector machine for video summarization

Vasileios Mygdalis, Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

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

12 Citations (Scopus)
221 Downloads (Pure)

Abstract

In this paper, we propose a method for video summarization based on human activity description. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. For this one-class classification problem, we introduce a novel variant of the One-Class Support Vector Machine (OC-SVM) classifier that exploits subclass information in the OC-SVM optimization problem, in order to jointly minimize the data dispersion within each subclass and determine the optimal decision function. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed SOC-SVM algorithm is compared with that of the OC-SVM. Experimental results denote that the proposed approach is able to outperform OC-SVM-based video segment selection.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015)
Subtitle of host publicationProceedings of a meeting held 19-24 April 2015, South Brisbane, Queensland, Australia
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2259-2263
Number of pages5
ISBN (Electronic)9781467369978
ISBN (Print)9781467369985
DOIs
Publication statusPublished - Sep 2015
EventIEEE International Conference on Accoustics, Speech and Signal Processing (ICASSP) - Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1520-6149

Conference

ConferenceIEEE International Conference on Accoustics, Speech and Signal Processing (ICASSP)
CountryAustralia
CityBrisbane
Period19/04/1524/04/15

Keywords

  • One class classification
  • Subclass One-Class SVM
  • Supervised Video Summarization

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