TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

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

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
KustantajaThe Institute of Electrical and Electronics Engineers, Inc.
Sivut2259-2263
Sivumäärä5
Vuosikerta2015-August
ISBN (painettu)9781467369978
DOI - pysyväislinkit
TilaJulkaistu - 4 elokuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Austraalia
Kesto: 19 huhtikuuta 201424 huhtikuuta 2014

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
MaaAustraalia
KaupunkiBrisbane
Ajanjakso19/04/1424/04/14

Tiivistelmä

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.