1. A method for a transductive multi-label classification, implemented at least in part by a computing device, the method comprising: utilizing a hidden Markov random field formulation to identify labels for concepts in a video content; modeling a multi-label interdependence between the labels by using a pairwise Markov random field, wherein combinations of pairwise relationships over the labels are seamlessly modeled into the hidden Markov random field formulation; grouping the labels into several parts to speed up a labeling inference; and calculating conditional probability scores for the labels, wherein the conditional probability scores are ordered for ranking in a video retrieval.
7. The method of claim 1 , further comprising using an optimization algorithm to infer the labels by combining: a tree-reweighted message passing, which is an iterative statistical inference algorithm; and graph cuts, which estimate an intermediate inductive decision function.