Multiple Instance Learning of Visual Event Models

Abstract

In this paper we propose a powerful visual event pattern learning method to address the issue of high-level video understanding. We first model the deformable temporal structure of the action event in videos by a temporal composition of several primitive motions. Moreover, we describe each action class by multiple temporal models to deal with the significant intra-class variability. We implement a multiple instance learning method to train the models in the weakly supervised setting. We have conducted experiments on three major benchmarks. The results are comparative to the state-of-the-arts.