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Big Data Analytics and Fuzzy Technology: Extracting Information from Social Data Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Automatic Image Classification for Web Content Filtering: New Dataset Evaluation

Authors : V. P. Fralenko, R. E. Suvorov, I. A. Tikhomirov

Published in: Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Publisher: Springer International Publishing

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Abstract

The paper presents experimental evaluation of image classification in the field of web content filtering using bag of visual features and convolutional neural networks approach. A more difficult data set than traditionally used ones was built from very similar types of images in order to make conditions closer to real world practice. F1-measure of classifiers that are based on bags of visual features was significantly lower than that reported in previously published papers. Convolutional neural networks performed much better. Also, we measured and compared training and prediction time of various algorithms.

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previous chapter Study of Soft Computing Methods for Large-Scale Multinomial Malware Types and Families Detection
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Literature
1.
R. Suvorov, I. Sochenkov, I. Tikhomirov, Method for pornography filtering in the web based on automatic classification and natural language processing, in Speech and Computer (Springer, 2013), pp. 233–240CrossRef
2.
R. Suvorov, I. Sochenkov, I. Tikhomirov, Training datasets collection and evaluation of feature selection methods for web content filtering, in Artificial Intelligence: Methodology, Systems, and Applications (Springer, 2014), pp. 129–138
3.
A.F. Drimbarean, P.M. Corcoran, M. Cuic, V. Buzuloiu, Image processing techniques to detect and filter objectionable images based on skin tone and shape recognition, in ICCE. International Conference on Consumer Electronics, 2001 (IEEE, 2001), pp. 278–279
4.
M. Hammami, Y. Chahir, L. Chen, Webguard: A web filtering engine combining textual, structural, and visual content-based analysis. IEEE Trans. Knowl. Data Eng. 18(2), 272–284 (2006)CrossRef
5.
B.-B. Liu, J.-Y. Su, Z.-M. Lu, Z. Li, Pornographic images detection based on cbir and skin analysis, in Fourth International Conference on Semantics, Knowledge and Grid (IEEE, 2008), pp. 487–488
6.
A. Zaidan, H.A. Karim, N. Ahmad, B. Zaidan, M.M. Kiah, Robust pornography classification solving the image size variation problem based on multi-agent learning. J. Circuits Syst. Comput. 24(02), 1550023 (2015)CrossRef
7.
J.-L. Shih, C.-H. Lee, C.-S. Yang, An adult image identification system employing image retrieval technique. Pattern Recogn. Lett. 28(16), 2367–2374 (2007)CrossRef
8.
T. Deselaers, L. Pimenidis, H. Ney, Bag-of-visual-words models for adult image classification and filtering, in 19th International Conference on Pattern Recognition, 2008. ICPR 2008 (IEEE, 2008), pp. 1–4
9.
A. Ulges, A. Stahl, Automatic detection of child pornography using color visual words, in 2011 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, 2011), pp. 1–6
10.
D.G. Lowe, Object recognition from local scale-invariant features, in The proceedings of the seventh IEEE international conference on Computer vision, 1999, vol. 2 (IEEE, 1999), pp. 1150–1157
11.
H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up robust features, in Computer Vision–ECCV 2006 (Springer, 2006), pp. 404–417CrossRef
12.
S.H. Yaghoubyan, M.A. Maarof, A. Zainal, M.F. Rohani, M.M. Oghaz, Fast and effective bag-of-visual-word model to pornographic images recognition using the freak descriptor. J. Soft Comput. Decis. Support Syst. 2(6), 27–33 (2015)
13.
Y. Fu, W. Wang, Fast and effectively identify pornographic images, in 2011 Seventh International Conference on Computational Intelligence and Security (CIS) (IEEE, 2011), pp. 1122–1126
14.
S.M. Kia, H. Rahmani, R. Mortezaei, M.E. Moghaddam, A. Namazi, A Novel Scheme for Intelligent Recognition of Pornographic Images, 2014. arXiv:​1402.​5792
15.
J. Polpinij, C. Sibunruang, S. Paungpronpitag, R. Chamchong, A. Chotthanom, A web pornography patrol system by content-based analysis: in particular text and image, in IEEE International Conference on Systems, Man and Cybernetics, 2008. SMC 2008 (IEEE, 2008), pp. 500–505
16.
U. Sayed, S. Sadek, B. Michaelis, Two phases neural network based system for pornographic image classification in Proceedings of the IEEE International Conference on Sciences of Electronic, Technologies of Information and Telecommunications, 2009, pp. 1–6
17.
J.A.M. Basilio, G.A. Torres, G. S´anchez, E. Hernadez, Explicit content image detection. Signal Image Process. Int. J. (SIPIJ) 1(2), 47–58 (2010)
18.
R. Mustafa, D. Zhu, A novel method for sensing obscene videos using scene change detection. TELKOMNIKA Indonesian J. Electr. Eng. 13(2), 300–304 (2015)
19.
20.
H. J´egou, M. Douze, C. Schmid, Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3), 316–336 (2010)
21.
G. Bradski, A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library (O’Reilly Media, Inc., 2008)
22.
Y. LeCun, Y. Bengio, Convolutional networks for images, speech, and time series, in The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10, 1995
23.
D.H. Hubel, T.N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106 (1962)CrossRef
24.
25.
M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the Rprop algorithm, in IEEE International Conference on Neural Networks, 1993, (IEEE, 1993), pp. 586–591
26.
B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the Fifth Annual Workshop on Computational Learning Theory (ACM, 1992), pp. 144–152
27.
C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH
28.
P.-H. Chen, C.-J. Lin, B. Schölkopf, A tutorial on ν-support vector machines. Appl. Stoch. Models Bus. Ind. 21(2), 111–136 (2005)MathSciNetCrossRef
29.
D.C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, in IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 1, 2011, p. 1237
30.
L.-H. Lee, Y.-C. Juan, W.-L. Tseng, H.-H. Chen, Y.-H. Tseng, Mining browsing behaviors for objectionable content filtering. J. Assoc. Inf. Sci. Technol. 66(5), 930–942 (2015)CrossRef
31.
S. Avila, N. Thome, M. Cord, E. Valle, A.D.A. Araujo, Pooling in image representation: the visual codeword point of view. Comput. Vis. Image Underst. 117(5), 453–465 (2013)CrossRef
32.
S. Becker, Y. Le Cun, Improving the convergence of backpropagation learning with second order methods, in Proceedings of the 1988 Connectionist Models Summer School (Morgan Kaufmann, San Matteo, CA, 1988), pp. 29–37
Metadata
Title
Automatic Image Classification for Web Content Filtering: New Dataset Evaluation
Authors
V. P. Fralenko
R. E. Suvorov
I. A. Tikhomirov
Copyright Year
2018
Book
Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-75407-9

Electronic ISBN: 978-3-319-75408-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75408-6_27

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