Bag of sub-graphs for video event recognition

Recognizing video events has been a very active field of interest. The diversity of videos captured in complex environments and under difficult conditions makes the event recognition a challenging task. In this paper, we present a video event recognition method which exploits the power of graphs for representing the structural organization of the features and the success of the Bag-of-Words approach. Our method combines the Scale Invariant Feature Transform and the Space-Time Interest Point features to characterize the video. To model the spatio-temporal relations among these features, a graph-based representation is used for each video. Then, the video is indexed based on a histogram of frequent sub-graphs. To evaluate our method, we have used the Columbia Consumer Video dataset. The experimental results show the efficiency of the proposed method.