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.
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Ballan, L., Bertini, M., Del Bimbo, A., Serra, G. (2009). Video Event Classification Using Bag of Words and String Kernels. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_20
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DOI: https://doi.org/10.1007/978-3-642-04146-4_20
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