1. A method for detecting two or more concepts in a source digital video which includes one or more digital video frames comprising: (a) segmenting the source digital video into a plurality of shots, wherein each shot includes one or more of the digital video frames; (b) identifying a keyframe within each shot, wherein the keyframe is one of the one or more of the digital video frames; (c) extracting low level features from the keyframe, wherein the low level features are representative of the two or more concepts, and are related in a graph of concepts, and wherein each concept is semi-automatically generated text associated with the one or more digital video frames, and wherein semi-automatically generated text is automatically generated text which has been manually revised; (d) training a discriminative classifier for each concept using a set of the low level features, wherein the discriminative classifier is a support vector machine; (e) building a collective annotation model combining each of the discriminative classifiers; (f) defining in the collective annotation model one or more interaction potential to model interdependence between related concepts; (g) receiving a second source digital video; (h) applying the discriminative classifiers to the second source digital video; and (i) determining a probability of a presence or absence of the two or more concepts in the low level features extracted from the second source digital video using the collective annotation model and the defined interaction potentials.
19. The method of claim 1 wherein the defined interaction potential is a function of each pair of related concepts Yi, Yj, wherein each pair of related concepts is connected by an edge in the graph of concepts, and wherein a total number of pairs of related concepts is less than a total number of pairs of concepts.