Human action recognition has been an active research topic in computer vision. How to model all kinds of actions, varying with time resolution, visual appearance, etc., is quite a challenging task for recognition. In this paper, we propose a Boosted Exemplar Learning (BEL) approach to recognize various actions in a weakly supervised manner, i.e., only video-based labels are provided but frame-based ones are not. First, for a given action, each video is described as a set of similarities between its frames and some candidate ones (called as exemplars), which are selected from training videos belonging to the action. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the Multiple Instance Learning (MIL), in which a positive (or negative) video is deemed as a positive (or negative) bag and those similar frames to the given exemplar in Euclidean Space as instances. Second, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain a video-based action detector in the boosted learning process. Experimental results on two publicly available challenging datasets: the KTH dataset and Weizmann dataset demonstrate the validity and effectiveness of the proposed approach.
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