Video Event Classification Using Bag of Words and String Kernels

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-ofwords (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. Key words: video annotation...

• Type: Journal
• Year: 2009
• Where: ICIAP
• Authors: Lamberto Ballan, Marco Bertini, Alberto Del Bimbo, Giuseppe Serra
• Keywords: Automatic Semantic Video, ICIAP 2009, Image Processing, Temporal Information, Video