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.
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. 8. The method of claim 1 , further comprising determining the multi-label interdependence between the labels by using concept pairs.