15. A method for a transductive multi-label classification, implemented at least in part by a computing device, the method comprising: detecting concepts in a video content by using a hidden Markov random field formulation to identify labels for the concepts by: determining the transductive multi-label classification by measuring similarity scores based on the labels and pre-given labels on labeled data points; determining the labels are consistent between neighboring points; determining a multi-label interdependence on unlabeled data points is similar to a multi-label interdependence on the labeled data points; and analyzing the concepts for the multi-label interdependence on the unlabeled data points and the multi-label interdependence on the labeled data points by using chunklet analysis.
15. A method for a transductive multi-label classification, implemented at least in part by a computing device, the method comprising: detecting concepts in a video content by using a hidden Markov random field formulation to identify labels for the concepts by: determining the transductive multi-label classification by measuring similarity scores based on the labels and pre-given labels on labeled data points; determining the labels are consistent between neighboring points; determining a multi-label interdependence on unlabeled data points is similar to a multi-label interdependence on the labeled data points; and analyzing the concepts for the multi-label interdependence on the unlabeled data points and the multi-label interdependence on the labeled data points by using chunklet analysis. 18. The method of claim 15 , wherein the chunklet analysis comprises actions to perform: measuring a relationship between each pair of labels according to a normalized mutual information (NMI); and pursuing common concepts that have large relationship degrees with other concepts.