New Method to Improve Illumination Variations in Adult Images Based on Fuzzy Deep Neural Network
Subject Areas : GeneralSasan Karamizadeh 1 , abouzar arabsorkhi 2
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Keywords: ILLUMINATION VARATION, SKIN CLASSIFICATION, ADULT IMAGE, SVM, GAUSSIAN-KNN,
Abstract :
In the era of the Internet, recognition of adult images is important to children's physical and mental protection. It is a challenge to recognize adult images with changes in the illumination and skin color. In this paper, we proposed a new method for solving illumination normalization with skin color classification in the diagnosis of the adult image. In this paper, the deep fuzzy neural network method is utilized to improve the illumination normalization of adult images, which has improved the recognization of adult images is utilized. Using Xception to dividing the images and reduce the illumination variations in each part separately, which makes it possible to reduce the illumination variation in the whole image without losing details. In addition, the advanced color combination algorithm based on Gaussian-KNN algorithm is used for skin color classification, a non-parametric method is used for classifications and regressions. Finally, the SVM algorithm is utilized for image classification. In this paper, 33,000 different types of images are collected from the Internet. The results show that the proposed method of 1/3 has improved the accuracy of the recognization.
1. Wang, H., and Fan, A.: ‘Pornographic information of Internet views detection method based on the connected areas’, in Editor (Ed.)^(Eds.): ‘Book Pornographic information of Internet views detection method based on the connected areas’ (International Society for Optics and Photonics, 2017, edn.), pp. 1032228
2.Wang, Y., Jin, X., and Tan, X.: ‘Pornographic image recognition by strongly-supervised deep multiple instance learning’, in Editor (Ed.)^(Eds.): ‘Book Pornographic image recognition by strongly-supervised deep multiple instance learning’ (IEEE, 2016, edn.), pp. 4418-4422
3.Adnan, A., and Nawaz, M.: ‘RGB and hue color in pornography detection’: ‘Information Technology: New Generations’ (Springer, 2016), pp. 1041-1050
4.Nian, F., Li, T., Wang, Y., Xu, M., and Wu, J.: ‘Pornographic image detection utilizing deep convolutional neural networks’, Neurocomputing, 2016, 210, pp. 283-293
5.Karamizadeh, S., Abdullah, S.M., Zamani, M., Shayan, J., and Nooralishahi, P.: ‘Face recognition via taxonomy of illumination normalization’: ‘Multimedia Forensics and Security’ (Springer, 2017), pp. 139-160
6.Brancati, N., De Pietro, G., Frucci, M., and Gallo, L.: ‘Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’ (Springer, 2016, edn.), pp. 27-35
7.Karamizadeha, S., Mabdullahb, S., Randjbaranc, E., and Rajabid, M.J.: ‘A review on techniques of illumination in face recognition’, Technology, 2015, 3, (02), pp. 79-83
8.Surinta, O., and Khamket, T.: ‘Recognizing pornographic images using deep convolutional neural networks’, in Editor (Ed.)^(Eds.): ‘Book Recognizing pornographic images using deep convolutional neural networks’ (IEEE, 2019, edn.), pp. 150-154
9.Noh, Y., Koo, D., Kang, Y.-M., Park, D., and Lee, D.: ‘Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’, in Editor (Ed.)^(Eds.): ‘Book Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’ (IEEE, 2017, edn.), pp. 877-880
10.Md Noor, S.S., Ren, J., Marshall, S., and Michael, K.: ‘Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries’, Sensors, 2017, 17, (11), pp. 2644
11.Gross, R., Baker, S., Matthews, I., and Kanade, T.: ‘Face recognition across pose and illumination’: ‘Handbook of face recognition’ (Springer, 2005), pp. 193-216
12. .Chen, Z., Liu, C., Chang, F., Han, X., and Wang, K.: ‘Illumination processing in face recognition’, International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28, (05), pp. 1456011
13.Basilio, J.A.M., Torres, G.A., Pérez, G.S., Medina, L.K.T., and Meana, H.M.P.: ‘Explicit image detection using YCbCr space color model as skin detection’, Clustering for Skin Detection Under Different Lighting Conditions’, in Editor (Ed.)^(Eds.): ‘Book Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions’ (Springer, 2016, edn.), pp. 27-35
7.Karamizadeha, S., Mabdullahb, S., Randjbaranc, E., and Rajabid, M.J.: ‘A review on techniques of illumination in face recognition’, Technology, 2015, 3, (02), pp. 79-83
8.Surinta, O., and Khamket, T.: ‘Recognizing pornographic images using deep convolutional neural networks’, in Editor (Ed.)^(Eds.): ‘Book Recognizing pornographic images using deep convolutional neural networks’ (IEEE, 2019, edn.), pp. 150-154
9.Noh, Y., Koo, D., Kang, Y.-M., Park, D., and Lee, D.: ‘Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’, in Editor (Ed.)^(Eds.): ‘Book Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering’ (IEEE, 2017, edn.), pp. 877-880
10.Md Noor, S.S., Ren, J., Marshall, S., and Michael, K.: ‘Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries’, Sensors, 2017, 17, (11), pp. 2644
11.Gross, R., Baker, S., Matthews, I., and Kanade, T.: ‘Face recognition across pose and illumination’: ‘Handbook of face recognition’ (Springer, 2005), pp. 193-216
12.Chen, Z., Liu, C., Chang, F., Han, X., and Wang, K.: ‘Illumination processing in face recognition’, International Journal of Pattern Recognition and Artificial Intelligence, 2014, 28, (05), pp. 1456011
13.Basilio, J.A.M., Torres, G.A., Pérez, G.S., Medina, L.K.T., and Meana, H.M.P.: ‘Explicit image detection using YCbCr space color model as skin detection’, Applications of Mathematics and Computer Engineering, 2011, pp. 123-128
14.Karamizadeh, S., and Arabsorkhi, A.: ‘Methods of pornography detection’, in Editor (Ed.)^(Eds.): ‘Book Methods of pornography detection’ (2018, edn.), pp. 33-38
15.Sufyanu, Z., Mohamad, F.S., Yusuf, A.A., and Mamat, M.B.: ‘Enhanced Face Recognition Using Discrete Cosine Transform’, Engineering Letters, 2016, 24, (1)
16.Liu, Z., Zhao, H., Pu, J., and Wang, H.: ‘Face recognition under varying illumination’, Neural Computing and Applications, 2013, 23, (1), pp. 133-139
17.Anagha, K., and Ram, A.R.: ‘Pose Tolerant Face Recognition: A Review’, in Editor (Ed.)^(Eds.): ‘Book Pose Tolerant Face Recognition: A Review’ (IEEE, 2020, edn.), pp. 0147-0152
18.Jin, X., Wang, Y., and Tan, X.: ‘Pornographic Image Recognition via Weighted Multiple Instance Learning’, IEEE transactions on cybernetics, 2018, 49, (12), pp. 4412-4420
19.Lopes, A.P., de Avila, S.E., Peixoto, A.N., Oliveira, R.S., and Araújo, A.d.A.: ‘A bag-of-features approach based on hue-sift descriptor for nude detection’, in Editor (Ed.)^(Eds.): ‘Book A bag-of-features approach based on hue-sift descriptor for nude detection’ (IEEE, 2009, edn.), pp. 1552-1556
20.Moustafa, M.: ‘Applying deep learning to classify pornographic images and videos’, arXiv preprint arXiv:1511.08899, 2015
21.Ding, X., Li, B., Li, Y., Guo, W., Liu, Y., Xiong, W., and Hu, W.: ‘Web Objectionable Video Recognition Based on Deep Multi Instance Learning with Representative Prototypes Selection’, IEEE Transactions on Circuits and Systems for Video Technology, 2020
