Segment-based Models for Event Detection and Recounting

We present a novel approach towards web video classification and recounting that uses video segments to model an event. This approach overcomes the limitations faced by the classical video-level models such as modeling semantics, identifying informative segments in a video and background segment suppression. We posit that segment-based models are able to identify both the frequently-occurring and rarer patterns in an event effectively, despite being trained on only a fraction of the training data. Our framework employs a discriminative approach to optimize our models in distributed and data-driven fashion while maintaining semantic interpretability. We evaluate the effectiveness of our approach on the challenging TRECVID MEDTest 2014 dataset. We demonstrate improvements in recounting and classification, particularly in events characterized by inherent intra-class variations.