A discriminative learning framework with pairwise constraints for video object classification

In video object classification, insufficient labeled data may at times be easily augmented with pairwise constraints on sample points, i.e, whether they are in the same class or not. In this paper, we proposed a discriminative learning approach, which incorporates pairwise constraints into a conventional margin-based learning framework. The proposed approach offers several advantages over existing approaches dealing with pairwise constraints. First, as opposed to learning distance metrics, the new approach derives its classification power by directly modeling the decision boundary. Second, most previous work handles labeled data by converting them to pairwise constraints and thus leads too much more computation. The proposed approach can handle pairwise constraints together with labeled data so that the computation is greatly reduced. The proposed approach is evaluated on a people classification task with two surveillance video datasets.