Pattern mining-based video saliency detection: Application to moving object segmentation

Abstract

In this paper, we present a new model for spatiotemporal saliency detection. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the pattern mining (PM) algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative spatiotemporal saliency patterns can be recognized and their label information is propagated to obtain the final saliency map. Our model ensures a good compromise between image saliency and motion saliency and presents an accurate prediction to estimate salient regions in comparison with other methods for video saliency detection. Finally, as an application of our algorithm, our spatiotemporal saliency map is combined with appearance models and dynamic location models into an energy minimization framework to segment salient moving object. Experiments show a good performance of our algorithm for moving object segmentation (MOS) on benchmark datasets.