Querying for video events by semantic signatures from few examples

We aim to query web video for complex events using only a handful of video query examples, where the standard approach learns a ranker from hundreds of examples. We consider a semantic signature representation, consisting of off-the-shelf concept detectors, to capture the variance in semantic appearance of events. Since it is unknown what similarity metric and query fusion to use in such an event retrieval setting, we perform three experiments on unconstrained web videos from the TRECVID event detection task. It reveals that: retrieval with semantic signatures using normalized correlation as similarity metric outperforms a low-level bag-of-words alternative, multiple queries are best combined using late fusion with an average operator, and event retrieval is preferred over event classification when less than eight positive video examples are available.