Skip to main content
SpringerLink
Log in

Improving performance of X-rated video classification with the optimized repeated curve-like spectrum feature and the skip-and-analysis processing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper addresses the automatic classification of X-rated videos by analyzing its obscene sounds. In this paper, we propose the optimized repeated curve-like spectrum feature for classifying obscene sounds and the skip-and-analysis processing for classifying videos. The optimized repeated curve-like spectrum feature uses the longer frame size for stationary frequency region based on the fact that most of obscene sounds, such as sexual moans and screams, consist of mostly vowels and the variation of syllables occurs slowly compared to general speech. It also uses the customized mel-scaled bandpass filter for the valid frequency regions of obscene sounds with the frequency contents mainly under 5 kHz. The skip-and-analysis processing is based on the video playback characteristics that a harmful or normal scene continues to be played at least for certain duration of time during a playback. When the skip-and-analysis processing is applied, clips to be analyzed are selected by skip interval values and only these selected clips are used to classify videos. The processing performances of the optimized repeated curve-like spectrum feature have improvements from 21 % to 25.6 % compared to the repeated curve-like spectrum feature without degradation of classification performance in clip-level classification. Furthermore, when the skip-and-analysis processing is applied, the processing performance of classifying is improved significantly by from 82.59 % to 95.03 % maintaining the classification performance of more than 90 % at F1-score.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bosson A, Cawley GC, Chan Y, Harvey R (2002) Non-retrieval: Blocking pornographic images. Proc. ACM International Conf. on Image and Video Retrieval, pp 50–60

  2. Brezeale D, Cook DJ (2008) Automatic video classification: a survey of the literature. IEEE Trans Syst Man Cybern Part C Appl Rev 38(3):416–430

    Article  Google Scholar 

  3. Cho DU (2004) Blocking of internet harmful pronographic sites by contents-based method. J Korea Info Commun Soc 29(6B):554–562

    Google Scholar 

  4. Coopersmith J (2000) Pornography, videotape, and the internet. IEEE Technol Soc Mag 19:27–34

    Google Scholar 

  5. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  6. Google Sage Search (2010) http://www.google.com. Accessed 15 March 2010

  7. Guo G, Li SZ (2003) Content-based audio classification and retrieval by support vector machine. IEEE Trans Neural Netw 14(1):209–215

    Article  Google Scholar 

  8. Hsu CW, Chang CC, Lin CJ (2009) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Accessed 30 Oct 2009

  9. Jansohn C, Ulges A, Breuel TM (2009) Detecting pornographic video content by combining image features with motion information. Proc. ACM International Conference on Multimedia

  10. Kamde PM, Algur SP (2011) A survey on web multimedia mining. Int J Multimed Appl 3(3):72–84

    Article  Google Scholar 

  11. Kim CY, Kwon OJ, Kim WG, Choi SR (2008) Automatic system for filtering obscene video. Proc. 10th International Conference on Advanced Communication Technology (ICACT2008). Pp 1435–1438

  12. Lee CH, Han CC, Chuang CC (2008) Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients. IEEE Trans Audio Speech Lang Process 16(8):1541–1550

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  14. Lim JD, Choi BC, Han SW, Chung BH, Lee CH (2011) Classification and detection of objectionable sounds using repeated curve-like spectrum feature. Proc. International Conf. on Information Science and Applications (ICISA2011), On-line Proceedings

  15. Lim JD, Choi BC, Han SW, Lee CH (2011) Automatic classification of x-rated videos using obscene sound analysis based on a repeated curve-like spectrum feature. Int J Multimed Appl 3(4):1–17

    Google Scholar 

  16. Lim JD, Han SW, Choi BC, Chung BH (2009) A study of classifying the objectionable contents by using the MFCCs-based audio feature set. Proc. 10th International Conf. on Computers, Communications, and Systems (ICCCS’09), pp 255–258

  17. Liu Z, Wang Y, Chen T (1998) Audio feature extraction and analysis for scene segmentation and classification. J VLSI Signal Proc Syst 20(1/2):61–79

    Article  Google Scholar 

  18. Lu L, Zhang HJ (2002) Content analysis for audio classification and segmentation. IEEE Transactions on speech and audio processing, Vol.10, pp 504–516. Vetro A, Sun H, DaGraca P, Poon T. Minimum drift architectures for three-layer scalable DTV decoding. IEEE Trans. Consumer Electron., vol. 44, no. 3, pp 527–536, Aug. 1998

  19. Lu L, Zhang HJ, Li SZ (2003) Content-based audio classification and segmentation by using support vector machines. ACM Multimedia Systems J 8(6):482–492

    Article  Google Scholar 

  20. Rao CS, Kumar SS, Mohan BC (2010) Content based image retrieval using exact legendre moments and support vector machine. Int J Multimed Appl 2(2):69–79

    Article  Google Scholar 

  21. Rea N, Lacey G, Lambe C, Dahyot R (2006) Multimodal periodicity analysis for illicit content detection in videos. Proc. IET Conference on Visual Media Production(CVMP2006), pp 106–114

  22. Rowley HA, Jing Y, Baluja S (2006) Large scale image-based adult content filtering. Proc. International Conf. on Computer Vision Theory and Applications (VISAPP), pp 290–296

  23. Secret Rabbit Code (2011) http://www.mega-nerd.com/SRC/ Accessed 1 June 2011

  24. Shigeo A (2005) Support vector machines for pattern classification. Springer, London

    Google Scholar 

  25. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  26. Wang JZ, Li J, Wiederhold G, Firschein O (1998) System for screening objectionable images. J Image Comput Commun 21:1355–1360

    Article  Google Scholar 

  27. Wang JC, Wang JF, Lin CB, Jian KT, Kuok WH (2006) Content-based audio classification using support vector machines and independent component analysis. Proc. 18th International Conf. on Pattern Recognition (ICPR06), Vol.4, pp 157–160

Download references

Acknowledgments

This research was supported by the KCC (Korea Communications Commission), Korea; under the ETRI R&D support program supervised by the KCA (Korea Communications Agency).” (KCA-11921-05001)

Author information

Authors and Affiliations

  1. Cyber Security Research Laboratory, ETRI, 218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Korea

    Jae-Deok Lim & Jeong-Nyeo Kim

  2. Multimodal&Human Interaction Lab. YM-Naeultech, 100 inha-ro, Nam-gu, Incheon, 402-751, Korea

    Young-Giu Jung

  3. Department of Design, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do, 200-701, Republic of Korea

    Young-Doo Yoon

  4. College of Computer Engineering, Chungnam National University (CNU), 99 Daehak-ro, Yuseong-gu, Daegeon, 305-764, Korea

    Cheol-Hoon Lee

Authors
  1. Jae-Deok Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeong-Nyeo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young-Giu Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Young-Doo Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cheol-Hoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheol-Hoon Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lim, JD., Kim, JN., Jung, YG. et al. Improving performance of X-rated video classification with the optimized repeated curve-like spectrum feature and the skip-and-analysis processing. Multimed Tools Appl 71, 717–740 (2014). https://doi.org/10.1007/s11042-013-1401-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1401-4

Keywords

Search

Navigation