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Analyzing periodicity and saliency for adult video detection

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Abstract

Content-based adult video detection plays an important role in preventing pornography. However, existing methods usually rely on single modality and seldom focus on multi-modality semantics representation. Addressing at this problem, we put forward an approach of analyzing periodicity and saliency for adult video detection. At first, periodic patterns and salient regions are respectively analyzed in audio-frames and visual-frames. Next, the multi-modal co-occurrence semantics is described by combining audio periodicity with visual saliency. Moreover, the performance of our approach is evaluated step by step. Experimental results show that our approach obviously outperforms some state-of-the-art methods.

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Acknowledgements

This work is supported by National Nature Science Foundation of China (61702179); Hunan Provincial Natural Science Foundation of China (2018JJ4052, 2017JJ2099, 2017JJ2081, 2017JJ3091).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Yizhi Liu, Junlin Ouyang, Miao Liao & Liangran Wu

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Xiaoyan Gu

  3. College of Information Science and Engineering, Ocean University of China, Shandong, 266100, China

    Lei Huang

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  1. Yizhi Liu
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  2. Xiaoyan Gu
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  3. Lei Huang
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  4. Junlin Ouyang
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Correspondence to Xiaoyan Gu.

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Liu, Y., Gu, X., Huang, L. et al. Analyzing periodicity and saliency for adult video detection. Multimed Tools Appl 79, 4729–4745 (2020). https://doi.org/10.1007/s11042-019-7576-6

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