Abstract
Comparing with the research of pornographic content filtering on Web, Web horror content filtering, especially horror video scene recognition is still on the stage of exploration. Most existing methods identify horror scene only from independent frames, ignoring the context cues among frames in a video scene. In this paper, we propose a Multi-view Multi-Instance Leaning (M2IL) model based on joint sparse coding technique that takes the bag of instances from independent view and contextual view into account simultaneously and apply it on horror scene recognition. Experiments on a horror video dataset collected from internet demonstrate that our method’s performance is superior to the other existing algorithms.
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Ding, X., Li, B., Hu, W., Xiong, W., Wang, Z. (2013). Horror Video Scene Recognition Based on Multi-view Multi-instance Learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_46
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DOI: https://doi.org/10.1007/978-3-642-37431-9_46
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