Unsupervised action classification using space-time link analysis

In this paper we address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which matches the performance of traditional unsupervised action categorization methods in a standard dataset. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatio-temporal relationships between the video features, while leveraging the power of graph matching for action classification. We present an experiment to demonstrate that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras.