基于内容的上网过滤方法研究
|
摘要
近年来随着网络技术的日益发展成熟,互联网上各种资源信息成倍的增加,其中网络色情信息的泛滥已经成为危害人们身心健康的全球性公害,基于信息内容的过滤问题逐渐引起人们的重视,为了阻止不健康信息的传播和净化网络环境,很多研究学者包括商业市场都对敏感信息的过滤领域做出了大量的研究,一部分商业过滤软件也有投入使用,但是其主要的过滤方法都局限于网址屏蔽和敏感关键字匹配技术,具有滞后性,误检率和漏检率都较高等诸多缺点,并且目前网络中绝大部分的敏感信息都是通过图片为载体来传递的,因此提出根据图像内容进行过滤才能从根本上解决目前网络安全技术对图像信息过滤与监控能力不足的问题。本文主要在前人研究的基础上,结合自己对于图片内容理解和过滤原理的研究成果,提出一种新的并有效的图片内容检测过滤器。本文的主要研究工作及成果如下:
(1)对图片格式的种类进行分析。本文主要介绍了目前互联网上图片格式:BMP、TIFF、GIF和JPEG等格式种类,并针对有损压缩图像中的JPEG图像的形成原理进行了介绍,为后文的基于压缩域的肤色检测提供理论基础。
(2)图片颜色空间和肤色检测模型的种类和研究介绍。图片颜色空间包括RGB、YCbCr、YIQ、YUV、YCgCr等,并对颜色空间之间的相互转换公式进行了介绍,以及肤色像素点在不同颜色空间中聚类性的实验研究。本文还介绍了三种肤色检测模型包括阈值化肤色模型、统计直方图肤色模型和混合高斯肤色模型,并对其肤色检测效果通过实验进行了比较,为基于不同光照自适应的肤色检测模型的选择提供实验依据。
(3)肤色纹理检测方法和图片分类模型的研究分析,肤色纹理检测方法包括一阶灰度统计法,主要原理是对肤色样本进行灰度统计,得到皮肤纹理的平均统计值与阈值进行比较判断。灰度共生矩阵法是从数学角度去研究图像纹理中像素灰度级的联合分布的统计量来表示纹理的空间相互依赖关系,Gabor滤波法通过对不同的频域信息全面的反映出图像空间的局部特征来进行纹理检测。还对三种敏感图片判定分类方法包括决策树分类法、支持向量机和BP神经网络分类法的判定原理进行了介绍和实验应用。?
(4)通过对图片内容中的肤色检测方法的研究为背景知识,提出两种不同肤色检测的方法,分别是提高检测精度的光照自适应的肤色检测方法和提高检测速度的基于模糊认知图的图像压缩域肤色检测方法,并通过实验证明两种肤色检测方法均在其专注领域取得了很好的肤色检测效果,以此肤色检测为基础的不健康图片过滤系统也取得了很好的过滤正确率。
通过大量实验数据表明,本文提出的敏感图像检测方法能够有效地过滤不健康图片并对图片内容的肤色检测也取得了很好的检测效果。
Along with the mature of network technology, all kinds of the resources have been increased multiples. Especially in picture and video message area. As a result porn message have overflowed in network and become a global hazard. Public have a strong interest in image filter based on its content to forbid this un-healthy message spread in network and rationalize the relations of network. And a lot of scholars which include business market have a deep research in image filter field, of course some commerce image filter software have been sold by company. But there have a bottleneck in filter which based on website and sensitive key works to block the un-healthy message. It brings some shortages like hysteretic, high fallout ration and high miss detection rates, etc. But most of un-healthy information is spread by images. We need raise a research direction on image filter based on content which can solve the root cause of the shortage of network safety technology on image filter and monitor. This paper concentrate on the image filter which based on the former scholars’work, combine with mine insight in image content and filter theory, then raise a new and effective filter tool for image. The research works are list below:
(1) Image format analysis. This paper have introduced popular image format at the moment: BMP,TIFF,GIF and JPEG, etc. And concentrate on the loss compression image which has a kind of current popular standard JPEG image. Given a brief introduction in compression and decompression theory which as a theory basement for image filter.
(2) Research on images’color space and skin detection model’s choice. Images’color space include RGB、YCbCr、YIQ、YUV、YCgCr, etc. There are also have an analysis on transform methods on different color space and skin pixel distributes compression level in different color space. There are also having introductions on three skin detection models: threshold skin detection model, statistics histogram skin model and mixed gauss skin model. And through a skin pixel test to get the examining rate to compare the three models’merit rating which offer the groundwork on illumination adaptive skin color detection method.
(3) Skin texture and image classification model analysis. Skin texture method include first order gray matrix, calculate the skin samples gray, then get the skin texture mean statistics to compare with the threshold value to get the result. Gray level co occurrence matrix in math angle to research images texture gray grade unite distribution for reaction the textures space inter rely on relation ship. Gabor filter method use different frequent region messages to reflect the image texture part of features. Image classifications include support vector machine, decision tree and BP nerve network classification.
(4) According to previous background analysis on image content detection, skin pixel verification and image classification. There have a deep insight on image filter based on its’content. And got a conclusion on images’classification. This paper raise two detect methods on image filter: illumination adaptive skin color detection method which concentrate on improve are examine rate, fuzzy cognitive map based skin detection method in compressed domain which focused on detection rate. Follow by an experiment to verify the filter rate, and test result prove two different methods all get a well result on detection.
Through the experiment method demonstrate the nude image filter method based on image content which raised in this paper get very well detection result.
(1)对图片格式的种类进行分析。本文主要介绍了目前互联网上图片格式:BMP、TIFF、GIF和JPEG等格式种类,并针对有损压缩图像中的JPEG图像的形成原理进行了介绍,为后文的基于压缩域的肤色检测提供理论基础。
(2)图片颜色空间和肤色检测模型的种类和研究介绍。图片颜色空间包括RGB、YCbCr、YIQ、YUV、YCgCr等,并对颜色空间之间的相互转换公式进行了介绍,以及肤色像素点在不同颜色空间中聚类性的实验研究。本文还介绍了三种肤色检测模型包括阈值化肤色模型、统计直方图肤色模型和混合高斯肤色模型,并对其肤色检测效果通过实验进行了比较,为基于不同光照自适应的肤色检测模型的选择提供实验依据。
(3)肤色纹理检测方法和图片分类模型的研究分析,肤色纹理检测方法包括一阶灰度统计法,主要原理是对肤色样本进行灰度统计,得到皮肤纹理的平均统计值与阈值进行比较判断。灰度共生矩阵法是从数学角度去研究图像纹理中像素灰度级的联合分布的统计量来表示纹理的空间相互依赖关系,Gabor滤波法通过对不同的频域信息全面的反映出图像空间的局部特征来进行纹理检测。还对三种敏感图片判定分类方法包括决策树分类法、支持向量机和BP神经网络分类法的判定原理进行了介绍和实验应用。?
(4)通过对图片内容中的肤色检测方法的研究为背景知识,提出两种不同肤色检测的方法,分别是提高检测精度的光照自适应的肤色检测方法和提高检测速度的基于模糊认知图的图像压缩域肤色检测方法,并通过实验证明两种肤色检测方法均在其专注领域取得了很好的肤色检测效果,以此肤色检测为基础的不健康图片过滤系统也取得了很好的过滤正确率。
通过大量实验数据表明,本文提出的敏感图像检测方法能够有效地过滤不健康图片并对图片内容的肤色检测也取得了很好的检测效果。
Along with the mature of network technology, all kinds of the resources have been increased multiples. Especially in picture and video message area. As a result porn message have overflowed in network and become a global hazard. Public have a strong interest in image filter based on its content to forbid this un-healthy message spread in network and rationalize the relations of network. And a lot of scholars which include business market have a deep research in image filter field, of course some commerce image filter software have been sold by company. But there have a bottleneck in filter which based on website and sensitive key works to block the un-healthy message. It brings some shortages like hysteretic, high fallout ration and high miss detection rates, etc. But most of un-healthy information is spread by images. We need raise a research direction on image filter based on content which can solve the root cause of the shortage of network safety technology on image filter and monitor. This paper concentrate on the image filter which based on the former scholars’work, combine with mine insight in image content and filter theory, then raise a new and effective filter tool for image. The research works are list below:
(1) Image format analysis. This paper have introduced popular image format at the moment: BMP,TIFF,GIF and JPEG, etc. And concentrate on the loss compression image which has a kind of current popular standard JPEG image. Given a brief introduction in compression and decompression theory which as a theory basement for image filter.
(2) Research on images’color space and skin detection model’s choice. Images’color space include RGB、YCbCr、YIQ、YUV、YCgCr, etc. There are also have an analysis on transform methods on different color space and skin pixel distributes compression level in different color space. There are also having introductions on three skin detection models: threshold skin detection model, statistics histogram skin model and mixed gauss skin model. And through a skin pixel test to get the examining rate to compare the three models’merit rating which offer the groundwork on illumination adaptive skin color detection method.
(3) Skin texture and image classification model analysis. Skin texture method include first order gray matrix, calculate the skin samples gray, then get the skin texture mean statistics to compare with the threshold value to get the result. Gray level co occurrence matrix in math angle to research images texture gray grade unite distribution for reaction the textures space inter rely on relation ship. Gabor filter method use different frequent region messages to reflect the image texture part of features. Image classifications include support vector machine, decision tree and BP nerve network classification.
(4) According to previous background analysis on image content detection, skin pixel verification and image classification. There have a deep insight on image filter based on its’content. And got a conclusion on images’classification. This paper raise two detect methods on image filter: illumination adaptive skin color detection method which concentrate on improve are examine rate, fuzzy cognitive map based skin detection method in compressed domain which focused on detection rate. Follow by an experiment to verify the filter rate, and test result prove two different methods all get a well result on detection.
Through the experiment method demonstrate the nude image filter method based on image content which raised in this paper get very well detection result.
引文
1. D.A.Forsytll,M.Fleck,and C.Bregler. Finding naked people[J].In Proceddings of the Fourth European Conference on Computer Vision , Cambridge,UK, 1996.593_602.
2. JONES M J,REHG J M. Statistical color models with application to skin detection[J]. Computer Vision,2002,46(1):81-96.
3. Hammami. M, Tsishkou. D, Liming Chen. Adult content Web filtering and face detection using data-mining based skin-color model[J]. IEEE International Conference, 2004, (1): 403 - 406.
4.许强,江早,赵宏。基于图像内容过滤的智能防火墙系统研究与实现[J]。计算机研究与发展,2000,37(4):458—464。
5.段立娟,崔国勤,高文,张洪明.多层次特定类型图像过滤方法[J].计算机辅助设计与图形学学报,2002,14(5):404-409.
6.胡冠宇.基于肤色之裸体影像侦测之研究[D]:[硕士学位论文].台南:国立成功大学,2004.
7.赵士伟,卓力,王素玉,沈兰荪.基于数据挖掘的图像压缩域的肤色检测算法[J].电子学报,2010,38(3):605-610.
8.章毓晋.图像处理和分析技术[M].第二版.高等教育出版社, 2008.1-2
9. Donald Hearn, M.pauline Baker. Computer graphics with openGL [M]. America:publishing house of electronics industry.2005, 573-584
10.郭玉红.静态图像压缩技术的研究与应用[D] : [硕士学位论文].天津:南开大学,2001
11.柳伯超.基于内容的不良图像识别研究[D]: [硕士学位论文].济南:山东师范大学,2007
12. Mihran Tacuryan,Aril K Jain.Texture Analysis.The Handbook of Pattern Recognition and Computer Vision[M].USA:World Scientific Publishing Co.1998:207-248
13. Ercan Oryildiz, Nils Krahnstover.,Rajeev Sharma. Adaptive Texture and Color Segmentation for Tracking Moving Objects[J], Pattern Recognition. 2002, 35(10):2013-2029
14. K R Castlemsn著,朱志刚等译.数字图像处理(第二版)[M].北京:电子工业出版社.2003
15. Haralick R.M . ,Sharmugam X. and Dinstein I . Textural Features for Image Classification[J].IEEE Transaction on Systems. 1973. 3: 610-621
16. Randen T, Husoy J H. Filtering for Texture Classification:A comparative study[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999.21(4):291-310
17. Tamura H, Nori S, Yamswaki T.Texture Features Corresponding to Visual Perception [J].IEEE Transactions on Systems,Man and Cybcrnetics.1978,8(6):460-473
18. Hsu R L, Mottaleb M A, Jain A K. Face detection in color image[J]. IEEE Transaction onpattern analysis and Machine Intelligence, 2002, 24(5): 696~706
19. Gerin H,H elliott.Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random fields[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 1987:39-55
20.张洪明,赵德斌,高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J].计算机学报, 2002, 25(11):1250-1256.
21. R.M.Haralick.. Statistical and Structural. Approaches to Texture[C].Proc. of IEEE.1979,67(5):45一69.
22. Kong W Z, Zhu S A. A New Method of Single Color Face Detection Based on Skin Model and Gaussian Distribution[C]. Proc of the 6th World Congress on Intelligent Control and Automation , 2006 :261-265.
23. R.Azencott, J.P.Wang and L.Younes, Texture Classification Using Windowed Fourier Filters[J], In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(2):148-153
24.刘佩如,复杂光照条件下的肤色检测研究[D].广州:华南理工大学,2010
25.粱路宏.人脸检测与跟踪研究[D].北京:清华大学计算机科学技术系, 2001
26.宋潇毅.基于纹理和颜色特征的图像检索[D] :[硕士学位论文].成都:电子科技大学, 2009
27. D.A.Clausi and M.E.Jernigan, Designing Gabor Filters for Optimal Texture Separability[J], In: Pattern Recognition, 2000, 33(11):1835-1840
28. A.Jain and G.Healey, A Multiscale Representation Including Opponent Color Features for Texture Recognition[J], IEEE Transactions on Image Processing, 1998, 7(1):124-128
29. C.Palm, D.Keysers, T.Lehmann et al.Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification[J], In: Proceedings of the joint conference on information sciences-International conference on computer vision, 2000, 2:45-59。
30.王慧琴著.数字图像处理[M].北京:北京邮电大学出版社. 2006
31. FINLAYSON G, HORDLEY S, HUBEL P M. Color by correlation: a simple, unifying framework for color constancy[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(11):1209 -1221.
32.郭耸,顾国昌,蔡则苏等.肤色相似度和动态阈值相结合的肤色分割技术[J].计算机工程与应用,2010,46(18):1-6.
33. DENG Y N, MANJUNATH B S, KENNEY C. An efficient color representation for image retrieval[J]. IEEE Transactions on Image Processing, 2001,10(1):140-147.
34. FLECK M M, FORSYTH D A, BREGLER C. Finding naked people[C].Proceedings of the 4th European Vision Conference on Computer,1996:593-602.
35.刘嘉勇,袁新峰.一种基于色阶偏差的皮肤检测技术研究[J].四川大学学报,2005.4(37):95-99.
36. YU.Yang, GUI Y F. Face deflection detection based on color information and projection[J]. Electronic Measurement Technology,2010,33(6):54-55.
37.段立娟,崔国勤,高文,等.多层次特定类型图像过滤方法[J].计算机辅助设计与图形学学报,2002,14(5):404-409.
38.易显东,唐丹,邓君,等.基于内容的特定图像过滤方法[J].计算机测量与控制,2004,12(3):283-286.
39.蒙丰博,张新荣,李学威.一种新的基于颜色恒常性的肤色分割算法[J].计算机工程与应用,2007,43(17):82-85.
40.王建国,林宇生,杨静宇.基于新颜色空间YCgCr的人脸区域初定位[J],计算机科学,2007,34(5):228-233.
41.付忠良.基于图像差距度量的阈值选取方法[J].计算机研究与发展,2001, 38(5):563-567.
42. Michael J. Jones and James M. Rehg. Statistical Color Model with Application to Skin Detection[J]. International Journal of Computer Vision, 2002,46(101)
43. Chair D, Ngan k N.Locating Facial Region of n Read and Shoulders Color Image[J].In Proceedings of the 3th International Conference on Automatic Face and Gesture Recognition Nara,1998,124-129.
44.雷明,张军荚,董济扬.一种可变光照条件下的肤色检测算[J].计算机工程与应用. 2002, 38(24):123-125
45. HsuR L, Abdel-Mattaleb M, Jain A.Face Detection in Color Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002,24(5):696-706
46.张明吉.自适应人体肤色检测中的若干关键技术研究[D]: [硕士学位论文].北京:国科学院计算技术研究所
47.张韬肤色检测算法的研究与实现[D]: [硕士学位论文].哈尔滨:哈尔滨工业大学
48. FINLAYSON G, HORDLEY S, HUBEL P M. Color by correlation: a simple, unifying framework for color constancy[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(11):1209 -1221.
49.郭耸,顾国昌,蔡则苏等.基于肤色分割和改进的AdaBoost SVM算法的人脸检测[J].吉林大学学报(工学报),2011,42(2):473-478.
50.王小鹏,闫国梁,闫文秀.基于最大熵分割和肤色模型的人眼定位[C].计算机工程,2010,36(14):161-165.
51.赵士伟,卓力,王素玉,沈兰荪.基于数据挖掘的图像压缩域的肤色检测算法[J].电子学报,2010,38(3):605-610.
52.于晓宇.静止图像压缩算法的研究[D]: [硕士学位论文].成都:电子科技大学,2009
53.张曦煌,卞国春,李红.基于统计特征的DCT压缩域纹理图像检索方法[J].计算机工程与设计,2006,27(7):1282-1285.
54. R L D Queiroz,R Eschbach.Fast segmentation of the JPEG compressed document[J]. Journal of Electronic Imaging, 1998, 7(2):367-377.
55. Q F Zheng,W Gao. Fast adaptive skin detection in JPEG images. Advances in Multimedia Information Processing [C]. Berlin/Heidbelerg: Springer,2005:595-605.
56. Augllar J.A survey about fuzzy cognitive maps papers [J]. International Journal of Computational Cognition,2005,3(2);27-33.
57.彭珍,杨炳儒,刘春梅,等.一种模糊认知图分类器的研究[J].计算机应用研究,2009,26(5):1757-1759.
58.李雄飞,李军数据挖掘与知识发现[M].北京:高等教育出版社.2003:12-13,161-173
59.范晓.基于浏览器的色情图片过滤器的设计和实现[D] :[硕士学位论文].长春:吉林大学, 2004: 30-31.
60. Leo Breiman, Jerome Friedman, Charies J.Stone. Classification and regression trees[M], USA: Chapman and Hall/CRC, 1984
61. K.P. Soman Shyam Diwakar V.Ajay著,范明,牛常勇译.数据挖掘基础教程[M].北京:机械工业出版社第一版.2009:40-51
62. Vapnik V N, The Nature of Statistical Learning Theory[M], New York: Springer-Verlag, 1995
63. Cortes C, Vapnik V, Support-Vector Networks[J], Machine Learning, 1995, 20(3): 273~297
64. C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition[J], Data Mining and Knowledge Discovery, 1998, 2: 121~167
65.秦锋,杨波,程泽凯,分类器性能评价标准研究[J].计算机技术与发展,2006,16(10): 85-88.
66.彭强,张晓飞。基于特征向量的敏感图像识别技术[J].西南交通大学学报,2007,42(1):13– 20.
67.唐怡亮。彩色图像分割技术及其在网络信息过滤中的应用[D]: [硕士学位论文].天津:南开大学, 2001: 28-33.
68.焦李成。神经网络系统理论[M].西安:西安电子科技大学出版社,1996。
69. Zurada J M, Introduction to Artificial Neural System, USA: West Publishing Company, 1992.
70.熊霞,桑庆兵。光照自适应的肤色检测方法[J].计算机应用,2005,5(31):1233-1236.
2. JONES M J,REHG J M. Statistical color models with application to skin detection[J]. Computer Vision,2002,46(1):81-96.
3. Hammami. M, Tsishkou. D, Liming Chen. Adult content Web filtering and face detection using data-mining based skin-color model[J]. IEEE International Conference, 2004, (1): 403 - 406.
4.许强,江早,赵宏。基于图像内容过滤的智能防火墙系统研究与实现[J]。计算机研究与发展,2000,37(4):458—464。
5.段立娟,崔国勤,高文,张洪明.多层次特定类型图像过滤方法[J].计算机辅助设计与图形学学报,2002,14(5):404-409.
6.胡冠宇.基于肤色之裸体影像侦测之研究[D]:[硕士学位论文].台南:国立成功大学,2004.
7.赵士伟,卓力,王素玉,沈兰荪.基于数据挖掘的图像压缩域的肤色检测算法[J].电子学报,2010,38(3):605-610.
8.章毓晋.图像处理和分析技术[M].第二版.高等教育出版社, 2008.1-2
9. Donald Hearn, M.pauline Baker. Computer graphics with openGL [M]. America:publishing house of electronics industry.2005, 573-584
10.郭玉红.静态图像压缩技术的研究与应用[D] : [硕士学位论文].天津:南开大学,2001
11.柳伯超.基于内容的不良图像识别研究[D]: [硕士学位论文].济南:山东师范大学,2007
12. Mihran Tacuryan,Aril K Jain.Texture Analysis.The Handbook of Pattern Recognition and Computer Vision[M].USA:World Scientific Publishing Co.1998:207-248
13. Ercan Oryildiz, Nils Krahnstover.,Rajeev Sharma. Adaptive Texture and Color Segmentation for Tracking Moving Objects[J], Pattern Recognition. 2002, 35(10):2013-2029
14. K R Castlemsn著,朱志刚等译.数字图像处理(第二版)[M].北京:电子工业出版社.2003
15. Haralick R.M . ,Sharmugam X. and Dinstein I . Textural Features for Image Classification[J].IEEE Transaction on Systems. 1973. 3: 610-621
16. Randen T, Husoy J H. Filtering for Texture Classification:A comparative study[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999.21(4):291-310
17. Tamura H, Nori S, Yamswaki T.Texture Features Corresponding to Visual Perception [J].IEEE Transactions on Systems,Man and Cybcrnetics.1978,8(6):460-473
18. Hsu R L, Mottaleb M A, Jain A K. Face detection in color image[J]. IEEE Transaction onpattern analysis and Machine Intelligence, 2002, 24(5): 696~706
19. Gerin H,H elliott.Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random fields[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 1987:39-55
20.张洪明,赵德斌,高文.基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测[J].计算机学报, 2002, 25(11):1250-1256.
21. R.M.Haralick.. Statistical and Structural. Approaches to Texture[C].Proc. of IEEE.1979,67(5):45一69.
22. Kong W Z, Zhu S A. A New Method of Single Color Face Detection Based on Skin Model and Gaussian Distribution[C]. Proc of the 6th World Congress on Intelligent Control and Automation , 2006 :261-265.
23. R.Azencott, J.P.Wang and L.Younes, Texture Classification Using Windowed Fourier Filters[J], In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(2):148-153
24.刘佩如,复杂光照条件下的肤色检测研究[D].广州:华南理工大学,2010
25.粱路宏.人脸检测与跟踪研究[D].北京:清华大学计算机科学技术系, 2001
26.宋潇毅.基于纹理和颜色特征的图像检索[D] :[硕士学位论文].成都:电子科技大学, 2009
27. D.A.Clausi and M.E.Jernigan, Designing Gabor Filters for Optimal Texture Separability[J], In: Pattern Recognition, 2000, 33(11):1835-1840
28. A.Jain and G.Healey, A Multiscale Representation Including Opponent Color Features for Texture Recognition[J], IEEE Transactions on Image Processing, 1998, 7(1):124-128
29. C.Palm, D.Keysers, T.Lehmann et al.Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification[J], In: Proceedings of the joint conference on information sciences-International conference on computer vision, 2000, 2:45-59。
30.王慧琴著.数字图像处理[M].北京:北京邮电大学出版社. 2006
31. FINLAYSON G, HORDLEY S, HUBEL P M. Color by correlation: a simple, unifying framework for color constancy[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(11):1209 -1221.
32.郭耸,顾国昌,蔡则苏等.肤色相似度和动态阈值相结合的肤色分割技术[J].计算机工程与应用,2010,46(18):1-6.
33. DENG Y N, MANJUNATH B S, KENNEY C. An efficient color representation for image retrieval[J]. IEEE Transactions on Image Processing, 2001,10(1):140-147.
34. FLECK M M, FORSYTH D A, BREGLER C. Finding naked people[C].Proceedings of the 4th European Vision Conference on Computer,1996:593-602.
35.刘嘉勇,袁新峰.一种基于色阶偏差的皮肤检测技术研究[J].四川大学学报,2005.4(37):95-99.
36. YU.Yang, GUI Y F. Face deflection detection based on color information and projection[J]. Electronic Measurement Technology,2010,33(6):54-55.
37.段立娟,崔国勤,高文,等.多层次特定类型图像过滤方法[J].计算机辅助设计与图形学学报,2002,14(5):404-409.
38.易显东,唐丹,邓君,等.基于内容的特定图像过滤方法[J].计算机测量与控制,2004,12(3):283-286.
39.蒙丰博,张新荣,李学威.一种新的基于颜色恒常性的肤色分割算法[J].计算机工程与应用,2007,43(17):82-85.
40.王建国,林宇生,杨静宇.基于新颜色空间YCgCr的人脸区域初定位[J],计算机科学,2007,34(5):228-233.
41.付忠良.基于图像差距度量的阈值选取方法[J].计算机研究与发展,2001, 38(5):563-567.
42. Michael J. Jones and James M. Rehg. Statistical Color Model with Application to Skin Detection[J]. International Journal of Computer Vision, 2002,46(101)
43. Chair D, Ngan k N.Locating Facial Region of n Read and Shoulders Color Image[J].In Proceedings of the 3th International Conference on Automatic Face and Gesture Recognition Nara,1998,124-129.
44.雷明,张军荚,董济扬.一种可变光照条件下的肤色检测算[J].计算机工程与应用. 2002, 38(24):123-125
45. HsuR L, Abdel-Mattaleb M, Jain A.Face Detection in Color Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002,24(5):696-706
46.张明吉.自适应人体肤色检测中的若干关键技术研究[D]: [硕士学位论文].北京:国科学院计算技术研究所
47.张韬肤色检测算法的研究与实现[D]: [硕士学位论文].哈尔滨:哈尔滨工业大学
48. FINLAYSON G, HORDLEY S, HUBEL P M. Color by correlation: a simple, unifying framework for color constancy[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(11):1209 -1221.
49.郭耸,顾国昌,蔡则苏等.基于肤色分割和改进的AdaBoost SVM算法的人脸检测[J].吉林大学学报(工学报),2011,42(2):473-478.
50.王小鹏,闫国梁,闫文秀.基于最大熵分割和肤色模型的人眼定位[C].计算机工程,2010,36(14):161-165.
51.赵士伟,卓力,王素玉,沈兰荪.基于数据挖掘的图像压缩域的肤色检测算法[J].电子学报,2010,38(3):605-610.
52.于晓宇.静止图像压缩算法的研究[D]: [硕士学位论文].成都:电子科技大学,2009
53.张曦煌,卞国春,李红.基于统计特征的DCT压缩域纹理图像检索方法[J].计算机工程与设计,2006,27(7):1282-1285.
54. R L D Queiroz,R Eschbach.Fast segmentation of the JPEG compressed document[J]. Journal of Electronic Imaging, 1998, 7(2):367-377.
55. Q F Zheng,W Gao. Fast adaptive skin detection in JPEG images. Advances in Multimedia Information Processing [C]. Berlin/Heidbelerg: Springer,2005:595-605.
56. Augllar J.A survey about fuzzy cognitive maps papers [J]. International Journal of Computational Cognition,2005,3(2);27-33.
57.彭珍,杨炳儒,刘春梅,等.一种模糊认知图分类器的研究[J].计算机应用研究,2009,26(5):1757-1759.
58.李雄飞,李军数据挖掘与知识发现[M].北京:高等教育出版社.2003:12-13,161-173
59.范晓.基于浏览器的色情图片过滤器的设计和实现[D] :[硕士学位论文].长春:吉林大学, 2004: 30-31.
60. Leo Breiman, Jerome Friedman, Charies J.Stone. Classification and regression trees[M], USA: Chapman and Hall/CRC, 1984
61. K.P. Soman Shyam Diwakar V.Ajay著,范明,牛常勇译.数据挖掘基础教程[M].北京:机械工业出版社第一版.2009:40-51
62. Vapnik V N, The Nature of Statistical Learning Theory[M], New York: Springer-Verlag, 1995
63. Cortes C, Vapnik V, Support-Vector Networks[J], Machine Learning, 1995, 20(3): 273~297
64. C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition[J], Data Mining and Knowledge Discovery, 1998, 2: 121~167
65.秦锋,杨波,程泽凯,分类器性能评价标准研究[J].计算机技术与发展,2006,16(10): 85-88.
66.彭强,张晓飞。基于特征向量的敏感图像识别技术[J].西南交通大学学报,2007,42(1):13– 20.
67.唐怡亮。彩色图像分割技术及其在网络信息过滤中的应用[D]: [硕士学位论文].天津:南开大学, 2001: 28-33.
68.焦李成。神经网络系统理论[M].西安:西安电子科技大学出版社,1996。
69. Zurada J M, Introduction to Artificial Neural System, USA: West Publishing Company, 1992.
70.熊霞,桑庆兵。光照自适应的肤色检测方法[J].计算机应用,2005,5(31):1233-1236.