A semantic framework for video genre classification and event analysis
Image Communication
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This paper proposes a novel semantic content analysis framework for reliable video event extraction which is essential for high-level video indexing and retrieval. In this work, we target to address the unique challenges posed in rare event detection, where positive examples (i.e., eventful data points) are vastly outnumbered and thus overshadowed by negative ones (i.e., noneventful data points). The proposed framework tackles this issue by integrating the strength of multimodal content analysis and neural network ensembles. Specifically, due to the rareness of the target events, the boostrapped sampling method is adopted to reduce the effect of class imbalance and a group of component neural networks are constructed consequently. Thereafter, a weighting scheme is applied to intelligently traverse and combine the component network predictions. The effectiveness of the proposed framework is demonstrated over a large collection of soccer video data with different styles produced by different broadcasters.