In search of video event semantics

In this thesis we aim to represent an event in a video using semantic features. We start from a bank of concept detectors for representing events in video. At first we considered the relevance of concepts to the event inside the video representation. We address the problem of video event classification using a bank of concept detectors. Different from existing work, which simply relies on a bank containing all available detectors, we propose an algorithm that learns from examples what concepts in bank are most informative per event. Secondly , we concentrated on the accuracy of concept detectors. Different from existing works, which obtain a semantic representation by training concepts over entire video clips, we propose an algorithm that learns a set of relevant frames as the concept prototypes from web video examples, without the need for frame-level annotations, and use them for representing an event video. Thirdly, we consider the problem of searching video events with concepts. We aim at querying web videos for events using only a handful of video query examples, where the standard approach learns a ranker from hundreds of examples. We consider a semantic representation, consisting of off-the-shelf concept detectors, to capture the variance in semantic appearance of events. Finally, we consider the problem of video event search without semantic concepts. The prevailing solutions in literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from them, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors.