Comparison of Performance between Different SVM Kernels for the Identification of Adult Video
In this paper we propose a method for recognition of
adult video based on support vector machine (SVM). Different kernel
features are proposed to classify adult videos. SVM has an advantage
that it is insensitive to the relative number of training example in
positive (adult video) and negative (non adult video) classes. This
advantage is illustrated by comparing performance between different
SVM kernels for the identification of adult video.
[1] J. Z. Wang, J. Li, G. Wiederhold, and O. Firschein, Classifying
objectionable websites based on image content, Special issue on
iteractive distributed multimedia systems and telecommunication
services, vol. 21/15, pp. 113-124, 1998.
[2] J.-C. Terrillon, M. N. Shirazi, H. Fukamachi, and S. Akamatsu, The
Comparative of different skin chrominance models for the automatic
detection of human faces in color images, Fourth International
Conference On Automatic Face and gesture Recognition, pp. 54-
61,2000
[3] DAE HYUN KIM and KIM Myoung-Jun, Skin color analysis in HSV
color space and rendering with fine scale skin structure, 24th Computer
Graphics International Conference, CGI 2006, Hangzhou, China, June
26-28, 2006.
[4] W. Kelly, A. Donnellan, D. Molloy A Review of Skin Detection
Techniques for Objectionable Images, Proc. 4th European Conf. on
Computer Vision, 2007.
[5] Chai, D. Phung, S.L. Bouzerdoum, A. Sch. of Eng. & Math., Edith
Cowan Univ., Perth, WA, Australia, A Bayesian skin/non-skin color
classifier using non-parametric density estimation, 25-28 May 2003.
[6] S. L. Phung, A. Sr. Bouzerdoum, and D. Sr. Chai, "Skin segmentation
using color pixel classification: Analysis and comparaison," IEEE
Transactions On Pattern Analysis and Machine Intelligence, vol. 27, pp.
148-154, 2005.
[7] Ming hsuan Yang and Narendra Ahuja, "Gaussian mixture model for
human skin color and its application in image and video databases," in
Proceedings of SPIE 99 (San Jose CA ), pp. 458-466, 1999.
[8] S. Makrogiannis P. Kakumanu and N. Bourbakis, "A survey of skincolor
modeling and detection methods," Pattern Recognition, vol. 40, no.
3, pp. 1106-1122, March 2007.
[9] S.L. Phung, A. Bouzerdoum, D. Chai, Skin segmentation using colour
pixel classification: analysis and comparison, IEEE Trans. Pattern Anal.
Mach. Intell, pages 148-154, 2005.
[10] V.Vezhnevets ,V. Sazonov, A . Andreeva, A Survey on Pixel-Based
Skin Color Detection Techniques, Machine Graphics & Vision
International Journal 2005.
[11] HUICHENG ZHENG, Blocking Objectionable Images: Adult Images
and Harmful Symbols, January 1998.
[12] Feng Jiao, Wen Giao, Lijuan Duan, and Guoqin Cui, Detecting adult
image using multiple features, IEEE conference, pp.378 - 383 vol.3,
Nov. 2001.
[13] Joachims T, Making Large-Scale SVM Learning Practical. LS8-Report,
University of Dortmund, LS 1998
[14] Vladimir VN, The Nature of Statistical Learning Theory. Springer,
Berlin Heidelberg New York, 1995.
[1] J. Z. Wang, J. Li, G. Wiederhold, and O. Firschein, Classifying
objectionable websites based on image content, Special issue on
iteractive distributed multimedia systems and telecommunication
services, vol. 21/15, pp. 113-124, 1998.
[2] J.-C. Terrillon, M. N. Shirazi, H. Fukamachi, and S. Akamatsu, The
Comparative of different skin chrominance models for the automatic
detection of human faces in color images, Fourth International
Conference On Automatic Face and gesture Recognition, pp. 54-
61,2000
[3] DAE HYUN KIM and KIM Myoung-Jun, Skin color analysis in HSV
color space and rendering with fine scale skin structure, 24th Computer
Graphics International Conference, CGI 2006, Hangzhou, China, June
26-28, 2006.
[4] W. Kelly, A. Donnellan, D. Molloy A Review of Skin Detection
Techniques for Objectionable Images, Proc. 4th European Conf. on
Computer Vision, 2007.
[5] Chai, D. Phung, S.L. Bouzerdoum, A. Sch. of Eng. & Math., Edith
Cowan Univ., Perth, WA, Australia, A Bayesian skin/non-skin color
classifier using non-parametric density estimation, 25-28 May 2003.
[6] S. L. Phung, A. Sr. Bouzerdoum, and D. Sr. Chai, "Skin segmentation
using color pixel classification: Analysis and comparaison," IEEE
Transactions On Pattern Analysis and Machine Intelligence, vol. 27, pp.
148-154, 2005.
[7] Ming hsuan Yang and Narendra Ahuja, "Gaussian mixture model for
human skin color and its application in image and video databases," in
Proceedings of SPIE 99 (San Jose CA ), pp. 458-466, 1999.
[8] S. Makrogiannis P. Kakumanu and N. Bourbakis, "A survey of skincolor
modeling and detection methods," Pattern Recognition, vol. 40, no.
3, pp. 1106-1122, March 2007.
[9] S.L. Phung, A. Bouzerdoum, D. Chai, Skin segmentation using colour
pixel classification: analysis and comparison, IEEE Trans. Pattern Anal.
Mach. Intell, pages 148-154, 2005.
[10] V.Vezhnevets ,V. Sazonov, A . Andreeva, A Survey on Pixel-Based
Skin Color Detection Techniques, Machine Graphics & Vision
International Journal 2005.
[11] HUICHENG ZHENG, Blocking Objectionable Images: Adult Images
and Harmful Symbols, January 1998.
[12] Feng Jiao, Wen Giao, Lijuan Duan, and Guoqin Cui, Detecting adult
image using multiple features, IEEE conference, pp.378 - 383 vol.3,
Nov. 2001.
[13] Joachims T, Making Large-Scale SVM Learning Practical. LS8-Report,
University of Dortmund, LS 1998
[14] Vladimir VN, The Nature of Statistical Learning Theory. Springer,
Berlin Heidelberg New York, 1995.
@article{"International Journal of Information, Control and Computer Sciences:62755", author = "Hajar Bouirouga and Sanaa El Fkihi and Abdeilah Jilbab and Driss Aboutajdine", title = "Comparison of Performance between Different SVM Kernels for the Identification of Adult Video", abstract = "In this paper we propose a method for recognition of
adult video based on support vector machine (SVM). Different kernel
features are proposed to classify adult videos. SVM has an advantage
that it is insensitive to the relative number of training example in
positive (adult video) and negative (non adult video) classes. This
advantage is illustrated by comparing performance between different
SVM kernels for the identification of adult video.", keywords = "Skin detection, Support vector machine,
Pornographic videos, Feature extraction, Video filtering,
Classification.", volume = "5", number = "5", pages = "521-4", }
