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Pornographic video detection with MapReduce

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Abstract

In the big data era, the video data of social media increase rapidly. To detect and block pornographic videos, traditional pornographic image detection methods cannot be applied directly to large-scaled video data. For this purpose, a parallel computing network has been set up by a lot of cheap computers for massive pornographic video data detection. First, we propose a key-frame extraction algorithm based on inter-frame similarity. This algorithm only uses the local information of the video and can be dispatched to multiple computers for parallel processing. The results of key-frame extraction are persisted to the distributed file system. Next, in order to determine whether a video contains pornographic key-frames, we propose a discriminative multiple Gaussian mixture models to extract skin color regions and an active relevance feedback bootstrap algorithm to detect the face. Finally, the geometric characteristics of the body are used to determine whether the key-frame is a pornographic image, and according to the number of pornographic key-frame in the video to decide whether the video is pornographic or not. Compared with some existing methods, the detection accuracy has been greatly improved. Because of the proposed methods are processed in different computer nodes for parallel computing, the processing speed is only related to the scale of the video data and the number of the computers. In practical applications, it can meet demands only need to select enough computers according to the scale of the video data. In theory, it can be used for video data at any scale.

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References

  1. http://royal.pingdom.com/2011/01/12/internet-2010-in-numbers/. Accessed 12 Jan 2015

  2. Jones MJ, Rehg JM (1999) Statistical Color Models with Application to Skin Detection. IEEE Conf Comput Vis Pattern Recognit 1:274–280

    Google Scholar 

  3. Zheng H, Daoudi M, Jedynak B (2004) Blocking adult images based on statistical skin detection. Electron Lett Comput Vis Image Anal 4(2):1–14

    Article  Google Scholar 

  4. Hu WM, Wu O, Chen ZY, Fu ZY, Maybank S (2007) Recognition of pornographic web pages by classifying texts and images. IEEE Trans Pattern Anal Mach Intell 29(6):1019–1034

    Article  Google Scholar 

  5. Sun HM (2010) Pornographic image screening by integrating recognition module and image black-list/white-list subsystem. IET Image Proc 4(2):103–113

    Article  Google Scholar 

  6. Sui L, Zhang J, Zhuo L, Yang YC (2012) “Research on pornographic images recognition method based on visual words in a compressed domain”. IET Image Proc 6(1):87–93

    Article  MathSciNet  Google Scholar 

  7. Fu YJ, Wang WQ (2011) Fast and effectively identify pornographic images. In: 2011 Seventh International Conference on Computational Intelligence and Security, pp 1122–1126

  8. Stephen B (2007) Google and the wisdom of clouds. http://4sis.com.br/noticias/Google and the Wisdom of Clouds.pdf. Accessed 21 May 2015

  9. Kusnetzy D (2011) IBM’s Virtualization and cloud computing plans. http://www.futureofenterprisecomputing.com/2011/05/ibm’s-virtualization-and-cloud-computing-plans/. Accessed 20 May 2015

  10. Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. Commun ACM 51:107–113

    Article  Google Scholar 

  11. Srirama SN, Jakovits P, Vainikko E (2012) Adapting scientific computing problems to clouds using MapReduce. Future Gener Comput Syst J 28(1):184–192

    Article  Google Scholar 

  12. Sande K, Gevers T, Snoek C (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596

    Article  Google Scholar 

  13. The Apache Software Foundation, “Hadoop”. http://hadoop.apache.org. Accessed 21 Jan 2016

  14. The Apache Software Foundation, “Hadoop Distributed File System”. http://hadoop.apache.org/docs/r0.20.2/hdfs_design. html. Accessed 23 Dec 2015

  15. Liu TM, Zhang HJ, Qi FH (2003) A novel video key frame extraction algorithm based on perceived motion energy model. IEEE Trans Circuits Syst Video Technol 13(10):1006–1013

    Article  Google Scholar 

  16. Liu LJ, Fan GL (2005) Combined key frame extraction and object based video segmentation. IEEE Trans Circuits Syst Video Technol 15(7):869–884

    Article  Google Scholar 

  17. Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Commun Appl 3(1):1–37

    Article  Google Scholar 

  18. Choudary C, Liu TC (2007) Summarization of visual content in instructional videos. IEEE Trans Multimed 9(7):1443–1455

    Article  Google Scholar 

  19. Li Z, Liu G, Qian X, Guo D, Jiang H (2011) Effective and efficient video text extraction using key text points. IET Image Proc 5(8):671–683

    Article  MathSciNet  Google Scholar 

  20. Kaur A, Kranthi BV (2012) Comparison between YCbCr color space and CIELab color space for skin color segmentation. Int J Adapt Innov Syst 3(4):30–33

    Google Scholar 

  21. Chitra S, Balakrishnan G (2012) Comparative study for two color spaces HSCbCr and YCbCr in skin color detection. Appl Math Sci 6(85):4229–4238

    Google Scholar 

  22. Bouman CA, Shapiro M, Cook GW, Atkins CB, Cheng H (1998) CLUSTER: an unsupervised algorithm for modeling gaussian mixtures. https://engineering.purdue.edu/~bouman/software/cluster/manual.pdf. Accessed 21 Feb 2016

  23. Verbeek J, Vlassis N, Krose B (2003) Efficient greedy learning of gaussian mixture. Neural Comput 15:469–485

    Article  Google Scholar 

  24. Dempster A, Laird N, Rubin D (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc Ser B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  25. Hsu RL, Mottaleb MA, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 24(5):696–706

    Article  Google Scholar 

  26. Gao XB, Zhong JJ, Li J, Tian CN (2008) Face sketch synthesis algorithm based on machine learning. IEEE Trans Circuits Syst Video Technol 18(4):487–496

    Article  Google Scholar 

  27. Amjad A, Griffiths A, Patwary MN (2012) Multiple face detection algorithm using color skin modeling. IET Image Proc 6(8):1093–1101

    Article  Google Scholar 

  28. Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Second European Conference on Computational Learning Theory (EuroCOLT ‘95), pp 23–37

    Google Scholar 

  29. http://baike.baidu.com/view/3084898.htm. Accessed 23 May 2015

  30. Lee JS, Kuo YM, Chung PC, Chen EL (2007) Naked image detection based on adaptive and extensible skin color model. Pattern Recognit 40:2261–2270

    Article  Google Scholar 

  31. Shih JL, Lee CH, Yang CS (2007) An adult image identification system employing image retrieval technique. Pattern Recognit Lett 28(16):2367–2374

    Article  Google Scholar 

  32. Jansohn C, Ulges A, Breuel TM (2009) Detecting pornographic video content by combining image features with motion information. In: ACM International Conference on Multimedia, pp. 601–604

  33. Lopes APB, Avila SEF, Peixoto ANA, Oliveira RS (2009) Nude detection in video using bag-of-visual-features. In: Brazilian Symposium on Computer Graphics and Image Processing, pp 224–231

  34. Ghomsheh AN, Talebpour A (2012) A new skin detection approach for adult image identification. Res J Appl Sci Eng Technol 4(21):4535–4545

    Google Scholar 

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Acknowledgements

Funding was provided by The National Science Fund for Distinguished Young Scholars (Grant No. 61125204).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jianqiang Yan & Xinbo Gao

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  1. Jianqiang Yan
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  2. Xinbo Gao
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Correspondence to Jianqiang Yan.

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Yan, J., Gao, X. Pornographic video detection with MapReduce. Int. J. Mach. Learn. & Cyber. 9, 2105–2115 (2018). https://doi.org/10.1007/s13042-017-0693-5

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