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.

Video Event Classification Using Bag of Words and String Kernels / Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe. - STAMPA. - 5716:(2009), pp. 170-178. ((Intervento presentato al convegno ICIAP tenutosi a Vietri sul Mare, Salerno nel September 8-11, 2009.

Video Event Classification Using Bag of Words and String Kernels

SERRA, GIUSEPPE
2009

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.
ICIAP
Vietri sul Mare, Salerno
September 8-11, 2009
5716
170
178
Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe
Video Event Classification Using Bag of Words and String Kernels / Lamberto, Ballan; Marco, Bertini; Alberto Del, Bimbo; Serra, Giuseppe. - STAMPA. - 5716:(2009), pp. 170-178. ((Intervento presentato al convegno ICIAP tenutosi a Vietri sul Mare, Salerno nel September 8-11, 2009.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/979926
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