Video Copy Detection Using a Soft Cascade of Multimodal Features

In the video copy detection task, it is widely recognized that none of any single feature can work well for all transformations. Thus more and more approaches adopt a set of complementary features to cope with complex audio-visual transformations. However, most of them utilize individual features separately and the final result is obtained by fusing results of several basic detectors. Often, this will lead to low detection efficiency. Moreover, there are some thresholds or parameters to be elaborately tuned. To address these problems, we propose a soft cascade approach to integrate multiple features for efficient copy detection. In our approach, basic detectors are organized in a cascaded framework, which processes a query video in sequence until one detector asserts it as a copy. To fully exert the complementarity of these detectors, a learning algorithm is proposed to estimate the optimal decision thresholds in the cascade architecture. Excellent performance on the benchmark dataset of TRECVid 2011 CBCD task demonstrates the effectiveness and efficiency of our approach.