CINECA IRIS Institutional Research Information System
IRIS è la piattaforma adottata dall’Università degli Studi di Udine per gestire l’archivio aperto dei prodotti della ricerca. L’archivio contiene articoli, contributi in volume, monografie, interventi pubblicati in atti di convegno, Tesi di dottorato e altri materiali.
EnglishThe recognition of events in videos is a relevant and challenging task of automatic semantic video analysis. At present one of the most successful frameworks, used for object recognition tasks, is the bag-of-words (BoW) approach. However this approach does not model the temporal information of the video stream. In this paper we present a method to introduce temporal information within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW model. The sequences are treated as strings where each histogram is considered as a character. Event classification of these sequences of variable size, depending on the length of the video clip, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two datasets, soccer video and TRECVID 2005, demonstrate the validity of the proposed approach. © 2009 Springer Berlin Heidelberg.
| Titolo: | Video event classification using bag of words and string kernels | |
| Autori: | ||
| Data di pubblicazione: | 2009 | |
| Serie: | ||
| Abstract: | The recognition of events in videos is a relevant and challenging task of automatic semantic video analysis. At present one of the most successful frameworks, used for object recognition tasks, is the bag-of-words (BoW) approach. However this approach does not model the temporal information of the video stream. In this paper we present a method to introduce temporal information within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW model. The sequences are treated as strings where each histogram is considered as a character. Event classification of these sequences of variable size, depending on the length of the video clip, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two datasets, soccer video and TRECVID 2005, demonstrate the validity of the proposed approach. © 2009 Springer Berlin Heidelberg. | |
| Handle: | http://hdl.handle.net/11390/1105598 | |
| ISBN: | 978-3-642-04146-4 978-3-642-04145-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11390/1105598
Copyright © 2022