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Boosting video classification using cross-video signals

We consider the problem of large-scale video classification. Our attention is focused on online video services since they can provide rich cross-video signals derived from user behavior. These signals help us to extract correlated information across videos which are co-browsed, co-uploaded, cocommented, co-queried, etc. Majority of the video classification methods omit this rich information and focus solely on a single test instance. In this paper, we propose a video classification system that exploits various cross-video signals offered by large-scale video databases. In our experiments, we show up to 4.5% absolute equal error rate (17% relative) improvement over the baseline on four video classification problems.