- Email: [email protected]
An Image Blocking Neural System for Children on the Internet
Copyright © IFAC Artificial Intelligence in Real-Time Control, Arizona, USA, 1998
An Image Blocking Neural System for Children on the Internet Yukari Kamakura, Yosbiyasu Takefuji Graduate School of Media & Governance Keio university 5322 Endo, Fujisawa Kanagawa 2520816 Japan [email protected], [email protected]
Abstract: This paper proposes an image blocking neural system on the Internet for children. The proposed system is used for discriminating an image whether it is a pornography or not The system is composed of two phases. In the first phase, image segments including naked human body are extracted from a target image using Munsell color order system. In the second phase, each of segments is classified into a pornography or others by using CombNet In our simulation, 42 tested images are used and compressed to 12x24 pixel respectively are used. Our system can automatically discriminate the pornography images with 67% accuracy. Copyrighl©J998lFAC Keywords: Image recognition, Pattern identification, Backpropagation, Self-organizing system, Classification, Hybrid vehicles
of web pages. Though QBIC(Query by Image Content) system developed by mM (http:// wwwqbic. almaden.ibmcoml) has functions to distinguish color, texture, and shapes of images, at final decision human discrimination is needed. Through the present social condition, it is highly demanded to have image-content based recognition systems. Margaret M.Aeck and David AForsyth have developed the system to identify naked people by only image-content(Aeck, et aI., 1996; Forsyth, et al.,I996). The algorithm is to identify the naked people by grouping or connecting with the cylindrical parts filtered by skin color. Their system obtains a good results of 43% accuracy through all processing. However it is difficult to recognize the images without human parts, arms and legs which are exposed skin. The goal of our system is to generate the good result of the clustered skin color by using CombNE!' (lwata, et aL, 1990; Iwata, et al., 1991) which is strong against the complicated pattern recognition.
1. IN1RODUcnON The explosion of the Internet causes serious problems on copyright, pornography, violence information and so on. 1be freedom of expression and the control is also one of the social problems in establishing security infrastructure for the network society. To solve the problems they come up to argwnents for the rules and mechanism among many countries. World Wide Web Consortium (W3C) has developed PICS (platform for Internet Content Selection) as a tool for solving the problem However, it does not work well to recognize the content of the image because the recognition system depends only on the tags of HTML (http://www.w3c.orglPics). Although several database companies have been developing the systems to search, rate and make URLdatabase including specific context or images, it is a time consuming task for the conventional manual systems to classify a lot of images. Net Shepherd corporation is the first experimental rating community of 1500 people who review the content (http://www.netshepherd.comlMedialmediahtm). However, they cannot catch up with the growth speed
2. OUR APPROACH The proposed system consists of two phases as
127
follows; In the first phase, image segments including the naked hwnan body are extracted from a target image according to the Hue, Chroma and Value of the Munsell color order system. In the second phase, each of segments is classified into a pornography or others by discriminating whether the area of skin color is clothed or not In the proposed model, Kohonen's self-organization and backpropagation neural network are combined to recognize a complicated image.
Va. In this system it is used its color, RGB=(221, 161,
117), which is the average of skin color by 100 images selected at random. Using the equation(2), the color images can change the grayscale images(Figurel) and make skin color clustering by making binary images at 230 of threshold shown as Figure2.
2.1 Extraction ofnaked human body The most of images on the Internet are colored and there are many noises in images so that it is important to specify the area of human body by skin color. And it is also needed to solve the problem to normalize the size and direction of the target image for image recognition. To make the problem simply this system deals with only the target images including one person which fronts and stands up.
Clustering skin color Figure I. Grayscale images changed from RGB color data by using Godlove Color Qustering Equation based on MunseII color order system
Many of researches for human detection use Munsell color order system for extraction of skin areas. Munsell color order system is defined as a model closed to human vision and expressed by Hue, Chroma, and Value. And its numerical value is able to convert images from RGB expression. The clustering model by Munsell color order system is defined called Godlove Color Oustering Equation shown as a following;
1 =~2C.C. {l-cos(6.H)} + (6. C) ' + (46. V)' 6. H
=!H a -
(1)
H b!
6.C=!C a -Cb! A V = !Va - Vb! It is said that human skin color is composed of the combination with blood(red) and melanin(yeIIow and brown), and the hue of human skin color is restricted (Rossotti, 1983) and also the value of color is pretty strong. For the reasons of the above mentioned the proposed system uses the follOwing equation changed Godlove Color Oustering Equation to make the hue more weighted and the value more lightened.
Figure 2. Binary images clustering by skin color
Extraction of a pan of human body Standard hwnan body size is able to shown as Figure 3. Under this size it is nearly possible to extract the part of human body using the principal axes of inertia and central moment The principal axes of inertia normalizes the
l' =~lOCQCb {1-cOS(AH)} + (l:!. C) 1 + (l:!. V)' (2)
It is the point to decide the standard skin color, Ha, Ca,
128
2.2 Discrimination naked body using CombNet
direction of the target image. A long axis shows vertical line of hwnan body and short one shows horizontal line of hwnan body. 'The central moment nearly points to the naval of the hwnan body. According to the measmement, each target image is able to extract pointed as nearly same place of hwnan body as Figure 4.
CombNET is the hybrid neural model combined with Kohonen's self-organization and backpropagation, developed for the recognition of hand-written characters. It is also implicated for the hwnan detection (Chashikawa, et aL, 1998). First part of selforganization is called Stem Network, and second part of backpropagation is called Branch Network. The model is suitable for extracting featmes of patterns in big fluctuations.
Processing by CombNet All input images, compressed 12x24 pixels, classified into several groups which contain similar pattern by Stem Network. And the result of the grouping data is discriminated by backpropagation for training of Branch Network. Branch Network is consists of three layered hierarchical Network. The output of the networks are between 0 and l(Figure 5). For the evaluation, if the output is above 0.5, it is identified naked hwnan body. And other is identified non-naked human body.
3. JESTED RESULT 14 images were prepared as naked body pattern and 28 images as non-naked human body pattern for training CombNET. Our result shows that with 67% we could correctly discriminated given images. Total Naked Images 14 NonNaked Images 28
Figure 3. Standard hwnan body
Correct Result 8
20
Accuracy% 57 67
71
4. DISCUSSION AND CONG..USION Our proposed system is effective to solve the problem by the result of 67% accuracy. However it still has some problems; sensitive our system is against the
images' light and shade. To avoid the problem. it is needed to increase the numbers of data and more learning or to make clustering more strictly by skin color. And other problems are; how to extract the featmes of 3 dimensional objects from static images, how to cope with enlargement, reduction and rotation of target images for pattern matching. A part of the 3-D problems might be solved by the model of M.Fleck (Fleck, et aI., 1996). And for the orientation imaging problem (enlargement, reduction and rotation), it might be solved by non-linear neural oscillation for human detection(Oka, et aL ,I998). Our proposed system doesn't handle well with the shape of the image recognition problem. Taking into these ideas, we may be able to improve the system ability for the target problem.
Figure 4. Extraction of a part of hwnan body
129
O:correct 1:incorrect
Stem Network
Grouping
Branch Network
Figure 5. CombNET system
REFERENCES Chashikawa, T, K.Fujii, YAjioka, Y.Takefuji(1998).
"Human Detection from Camera-Images by CombNET", Proc. of EANlV98. Forsyth, D. A., Margaret M.Fleck(l996). "Identifying Nude PiCtln'eS", Proc. Third IEEE Workshop on Applications of Computer Vision. WACV'96. Fleck, M.M., David A.Forsyth, Chris Bregler(I996). "Finding Naked People''.European Conference on Computer VISion, vol II, pp.592-602. Iwata, A., T.Tohma, H.Matsuo, N.Suzumura(I990). "A Large Scaled Neural Network"CombNet" and its application to Chinese Character Recognition", Proc.ofINNS90. lwata, A., K.Hotta, H.Matsuo N.Suzumura,(l99I). "A Large Scaled Neural Network"CombNet" ", Proc. of IATSTED, July, 1991. Oka, S, YAjioka, Y.Takefuji(1998)."A Self-Organized Oscillation in Detecting TlIDe-Varing Human Faces", Proc. ofEANlV98. Rossotti, HazeI(l983)."C%r: Why the World isn't Grey", Princeton University Press, NI.
130
An Image Blocking Neural System for Children on the Internet Yukari Kamakura, Yosbiyasu Takefuji Graduate School of Media & Governance Keio university 5322 Endo, Fujisawa Kanagawa 2520816 Japan [email protected], [email protected]
Abstract: This paper proposes an image blocking neural system on the Internet for children. The proposed system is used for discriminating an image whether it is a pornography or not The system is composed of two phases. In the first phase, image segments including naked human body are extracted from a target image using Munsell color order system. In the second phase, each of segments is classified into a pornography or others by using CombNet In our simulation, 42 tested images are used and compressed to 12x24 pixel respectively are used. Our system can automatically discriminate the pornography images with 67% accuracy. Copyrighl©J998lFAC Keywords: Image recognition, Pattern identification, Backpropagation, Self-organizing system, Classification, Hybrid vehicles
of web pages. Though QBIC(Query by Image Content) system developed by mM (http:// wwwqbic. almaden.ibmcoml) has functions to distinguish color, texture, and shapes of images, at final decision human discrimination is needed. Through the present social condition, it is highly demanded to have image-content based recognition systems. Margaret M.Aeck and David AForsyth have developed the system to identify naked people by only image-content(Aeck, et aI., 1996; Forsyth, et al.,I996). The algorithm is to identify the naked people by grouping or connecting with the cylindrical parts filtered by skin color. Their system obtains a good results of 43% accuracy through all processing. However it is difficult to recognize the images without human parts, arms and legs which are exposed skin. The goal of our system is to generate the good result of the clustered skin color by using CombNE!' (lwata, et aL, 1990; Iwata, et al., 1991) which is strong against the complicated pattern recognition.
1. IN1RODUcnON The explosion of the Internet causes serious problems on copyright, pornography, violence information and so on. 1be freedom of expression and the control is also one of the social problems in establishing security infrastructure for the network society. To solve the problems they come up to argwnents for the rules and mechanism among many countries. World Wide Web Consortium (W3C) has developed PICS (platform for Internet Content Selection) as a tool for solving the problem However, it does not work well to recognize the content of the image because the recognition system depends only on the tags of HTML (http://www.w3c.orglPics). Although several database companies have been developing the systems to search, rate and make URLdatabase including specific context or images, it is a time consuming task for the conventional manual systems to classify a lot of images. Net Shepherd corporation is the first experimental rating community of 1500 people who review the content (http://www.netshepherd.comlMedialmediahtm). However, they cannot catch up with the growth speed
2. OUR APPROACH The proposed system consists of two phases as
127
follows; In the first phase, image segments including the naked hwnan body are extracted from a target image according to the Hue, Chroma and Value of the Munsell color order system. In the second phase, each of segments is classified into a pornography or others by discriminating whether the area of skin color is clothed or not In the proposed model, Kohonen's self-organization and backpropagation neural network are combined to recognize a complicated image.
Va. In this system it is used its color, RGB=(221, 161,
117), which is the average of skin color by 100 images selected at random. Using the equation(2), the color images can change the grayscale images(Figurel) and make skin color clustering by making binary images at 230 of threshold shown as Figure2.
2.1 Extraction ofnaked human body The most of images on the Internet are colored and there are many noises in images so that it is important to specify the area of human body by skin color. And it is also needed to solve the problem to normalize the size and direction of the target image for image recognition. To make the problem simply this system deals with only the target images including one person which fronts and stands up.
Clustering skin color Figure I. Grayscale images changed from RGB color data by using Godlove Color Qustering Equation based on MunseII color order system
Many of researches for human detection use Munsell color order system for extraction of skin areas. Munsell color order system is defined as a model closed to human vision and expressed by Hue, Chroma, and Value. And its numerical value is able to convert images from RGB expression. The clustering model by Munsell color order system is defined called Godlove Color Oustering Equation shown as a following;
1 =~2C.C. {l-cos(6.H)} + (6. C) ' + (46. V)' 6. H
=!H a -
(1)
H b!
6.C=!C a -Cb! A V = !Va - Vb! It is said that human skin color is composed of the combination with blood(red) and melanin(yeIIow and brown), and the hue of human skin color is restricted (Rossotti, 1983) and also the value of color is pretty strong. For the reasons of the above mentioned the proposed system uses the follOwing equation changed Godlove Color Oustering Equation to make the hue more weighted and the value more lightened.
Figure 2. Binary images clustering by skin color
Extraction of a pan of human body Standard hwnan body size is able to shown as Figure 3. Under this size it is nearly possible to extract the part of human body using the principal axes of inertia and central moment The principal axes of inertia normalizes the
l' =~lOCQCb {1-cOS(AH)} + (l:!. C) 1 + (l:!. V)' (2)
It is the point to decide the standard skin color, Ha, Ca,
128
2.2 Discrimination naked body using CombNet
direction of the target image. A long axis shows vertical line of hwnan body and short one shows horizontal line of hwnan body. 'The central moment nearly points to the naval of the hwnan body. According to the measmement, each target image is able to extract pointed as nearly same place of hwnan body as Figure 4.
CombNET is the hybrid neural model combined with Kohonen's self-organization and backpropagation, developed for the recognition of hand-written characters. It is also implicated for the hwnan detection (Chashikawa, et aL, 1998). First part of selforganization is called Stem Network, and second part of backpropagation is called Branch Network. The model is suitable for extracting featmes of patterns in big fluctuations.
Processing by CombNet All input images, compressed 12x24 pixels, classified into several groups which contain similar pattern by Stem Network. And the result of the grouping data is discriminated by backpropagation for training of Branch Network. Branch Network is consists of three layered hierarchical Network. The output of the networks are between 0 and l(Figure 5). For the evaluation, if the output is above 0.5, it is identified naked hwnan body. And other is identified non-naked human body.
3. JESTED RESULT 14 images were prepared as naked body pattern and 28 images as non-naked human body pattern for training CombNET. Our result shows that with 67% we could correctly discriminated given images. Total Naked Images 14 NonNaked Images 28
Figure 3. Standard hwnan body
Correct Result 8
20
Accuracy% 57 67
71
4. DISCUSSION AND CONG..USION Our proposed system is effective to solve the problem by the result of 67% accuracy. However it still has some problems; sensitive our system is against the
images' light and shade. To avoid the problem. it is needed to increase the numbers of data and more learning or to make clustering more strictly by skin color. And other problems are; how to extract the featmes of 3 dimensional objects from static images, how to cope with enlargement, reduction and rotation of target images for pattern matching. A part of the 3-D problems might be solved by the model of M.Fleck (Fleck, et aI., 1996). And for the orientation imaging problem (enlargement, reduction and rotation), it might be solved by non-linear neural oscillation for human detection(Oka, et aL ,I998). Our proposed system doesn't handle well with the shape of the image recognition problem. Taking into these ideas, we may be able to improve the system ability for the target problem.
Figure 4. Extraction of a part of hwnan body
129
O:correct 1:incorrect
Stem Network
Grouping
Branch Network
Figure 5. CombNET system
REFERENCES Chashikawa, T, K.Fujii, YAjioka, Y.Takefuji(1998).
"Human Detection from Camera-Images by CombNET", Proc. of EANlV98. Forsyth, D. A., Margaret M.Fleck(l996). "Identifying Nude PiCtln'eS", Proc. Third IEEE Workshop on Applications of Computer Vision. WACV'96. Fleck, M.M., David A.Forsyth, Chris Bregler(I996). "Finding Naked People''.European Conference on Computer VISion, vol II, pp.592-602. Iwata, A., T.Tohma, H.Matsuo, N.Suzumura(I990). "A Large Scaled Neural Network"CombNet" and its application to Chinese Character Recognition", Proc.ofINNS90. lwata, A., K.Hotta, H.Matsuo N.Suzumura,(l99I). "A Large Scaled Neural Network"CombNet" ", Proc. of IATSTED, July, 1991. Oka, S, YAjioka, Y.Takefuji(1998)."A Self-Organized Oscillation in Detecting TlIDe-Varing Human Faces", Proc. ofEANlV98. Rossotti, HazeI(l983)."C%r: Why the World isn't Grey", Princeton University Press, NI.
130