Video event classification using string kernels

Event recognition is a crucial task to provide high-level semantic description of the video content. The bag-of-words (BoW) approach has proven to be successful for the categorization of objects and scenes in images, but it is unable to model temporal information between consecutive frames. In this paper we present a method to introduce temporal information for video event recognition within the BoW approach. Events are modeled as a sequence composed of histograms of visual features, computed from each frame using the traditional BoW. The sequences are treated as strings (phrases) where each histogram is considered as a character. Event classification of these sequences of variable length, depending on the duration of the video clips, are performed using SVM classifiers with a string kernel that uses the Needlemann-Wunsch edit distance. Experimental results, performed on two domains, soccer videos and a subset of TRECVID 2005 news videos, demonstrate the validity of the proposed approach.