Multi-View Dynamic Texture Learning

Abstract

Dynamic texture (DT) provides a flexible and suitable tool for representing phenomena over space and time. We focus here on DT learning for multi-view domains, each of which is sufficient to learn the target concept. We make several contributions in this paper. First, we derive new features and then present their use in our description of DT. Second, we introduce multi-view dynamic texture learning, which aims to combine several views to achieve a more reliable and accurate result. The core of this multi-view combination is based on the recent information theories of the probabilistic rand index. Third, we present a way to impose spatial smoothness constraints between neighboring observations. Finally, we empirically show that our model outperforms existing state-of-the-art methods recently proposed in the literature on various datasets.