Multimedia Event Detection Using a Classifier-specific Intermediate Representation

Multimedia event detection (MED) plays an important role in many applications such as video indexing and retrieval. Current event detection works mainly focus on sports and news event detection or abnormality detection in surveillance videos. Differently, our research aims to detect more complicated and generic events within a longer video sequence. In the past, researchers have proposed using intermediate concept classifiers with concept lexica to help understand the videos. Yet it is difficult to judge how many and what concepts would be sufficient for the particular video analysis task. Additionally, obtaining robust semantic concept classifiers requires a large number of positive training examples, which in turn has high human annotation cost. In this paper, we propose an approach that exploits the external concepts-based videos and event-based videos simultaneously to learn an intermediate representation from video features. Our algorithm integrates the classifier inference and la...