Abstract
With the advent of the Internet along with sophisticated digital image processing technology, the Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the advent of GPRS mobile telephone networks, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, drives us toward a new degree of complexity. The detection of pornography remains an important and significant research problem, since there is great potential to minimize harm to the community. In this paper, we propose a novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on most commonly found erotic poses. Compared to the results published in recent works, our proposed approach yields the highest accuracy in recognition.
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Ropelato J (2009) Internet pornography statistics http://internet-filter-review.toptenreviews.com/internet-pornography-statistics.html Accessed March 2009
McGuire RJ, Carlisle JM, Young BG (1964) Sexual deviations as conditioned behaviour: a hypothesis. Behav Res Ther 2:185–190
Silbert MH, Pines AM (1984) Pornography and sexual abuse of women. Sex Roles 10:857–868
Carter DL, Prentky RA, Knight RA, Vanderveer PL, Boucher RJ (1987) Use of pornography in the criminal and developmental histories of sexual offenders. J Interpers Violence 2:196–211
Alexy EM, Ann WB, Robert AP (2009) Pornography use as a risk marker for an aggressive pattern of behavior among sexually reactive children and adolescents. J Am Psychiatr Nurses Assoc 14(6):442–453
Ruiz-del-Solar J, Castañeda V, Verschae R, Baeza-Yates R, Ortiz F (2005) Characterizing objectionable image content (pornography and nude images) of Speci_cWeb segments: chile as a case study. In: Proceedings of the third Latin American web congress (LA-WEB’05, IEEE)
Jeong C, Kim J, Hong K (2004) Appearance-based nude image detection. In: Proceedings of the 17th international conference on pattern recognition (ICPR’04), IEEE Computer Society, Washington, DC, USA
Zheng QF, Zeng W, Wang WQA, Gao W (2006) Shape-based adult image detection. Int J Image Graphics (IJIG) 6(1):115–124
Chai D, Bouzerdoum A (2000) A Bayesian approach to skin color classification in YCbCr color space. In: IEEE TENCON00, vol 2, pp 421–424
Soriano M, MartinKauppi JB, Huovinen S, Laaksonen M (2003) Adaptive skin color modeling using the skin locus for selecting training pixels. Pattern Recognit 36(3):681–690
Fleck M, Forsyth A, Bregler C (1996) Finding naked people. In Computer Vision—ECCV’96, 4th European conference on computer vision, Cambridge, UK, vol II, pp 593–602
Lee J-S, Kuo Y-M, Chung P-C, Chen E-L (2007) Naked image detection based on adaptive and extensible skin colour model. Pattern Recognit 40(8):2261–2270
Wang Y, Wang W, Gao1 W (2005) Research on the discrimination of pornographic and bikini images. In: Proccedings of the 7th IEEE international symposium on multimedia (ISM’05)
Islam M, Watters P, Yearwood J, Hussain M, Swarna L (2011) Illicit image detection: an MRF model based stochastic approach. In: CISSE’2011, Bridgeport, USA
Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by tracking. In: CVPR
Ferrari V, Marin-Jimenez M, Zisserman A Progressive search space reduction for human pose estimation. In: CVPR, Jun 2008
Gammeter S, Ess A, Jaeggli T, Schindler K, Van Gool L (2008) Articulated multi-body tracking under egomotion. In: ECCV
Felzenszwalb P, Huttenlocher D (2005) Pictorial structures for object recognition. IJCV 61(1):55–79
Islam M (2008) Unsupervised color image segmentation using Markov random fields model. Master by Research Thesis, Graduate School of Information Technology and Mathematical Sciences, University of Ballarat, Australia
Ferrari V, Marin-Jimenez M, Zisserman A (2008) Progressive search space reduction for human pose estimation. In: CVPR, Jun 2008
Eichner M, Ferrari V (2009) Better appearance models for pictorial structures. In: British machine vision conference, London, UK
Ramanan D (2006) Learning to parse images of articulated bodies. In NIPS
Belem RJS, Cavalcanti JMB (2005) SNIF: a simple nude image finder. In: Proceedings of the third Latin American web congress (LA-WEB’05), IEEE
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Islam, M., Watters, P., Yearwood, J., Hussain, M., Swarna, L.A. (2013). Illicit Image Detection Using Erotic Pose Estimation Based on Kinematic Constraints. In: Elleithy, K., Sobh, T. (eds) Innovations and Advances in Computer, Information, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3535-8_41
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DOI: https://doi.org/10.1007/978-1-4614-3535-8_41
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