Accurate video concept recognition via classifier combination

摘要

A classifier learning module trains video classifiers associated with a stored set of concepts derived from textual metadata of a plurality of videos. Specifically, a first type of classifier (e.g., a content-based classifier) and a second type of classifier (e.g., a text-based classifier) are trained, the classifiers when applied to a video indicating a likelihood that the video represents one or more concepts corresponding to the classifier. The first type of classifier can be used to determine the training set for the second type of classifier. The learning process does not require any concepts to be known a priori, nor does it require a training set of videos having training labels manually applied by human experts. Scores from the first type of classifier are combined with scores from the second type of classifier to obtain video classification of enhanced accuracy.