Learning frame relevance for video classification

Traditional video classification methods typically require a large number of labeled training video frames to achieve satisfactory performance. However, in the real world, we usually only have sufficient labeled video clips (such as tagged online videos) but lack labeled video frames. In this paper, we formalize the video classification problem as a Multi-Instance Learning (MIL) problem, an emerging topic in machine learning in recent years, which only needs bag (video clip) labels. To solve the problem, we propose a novel Parameterized Class-to-Bag (P-C2B) Distance method to learn the relative importance of a training instance with respect to its labeled classes, such that the instance level labeling ambiguity in MIL is tackled and the frame relevances of training video data with respect to the semantic concepts of interest are given. Promising experimental results have demonstrated the effectiveness of the proposed method.