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Automatic Image Classification for Web Content Filtering: New Dataset Evaluation

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

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 361))

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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References

  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. 233–240

    Chapter  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  11. H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up robust features, in Computer Vision–ECCV 2006 (Springer, 2006), pp. 404–417

    Chapter  Google Scholar 

  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)

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  19. M. Moustafa, in Applying Deep Learning to Classify Prnographic Images and Videos, 2015. arXiv:1511.08899

  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)

    Google Scholar 

  21. G. Bradski, A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library (O’Reilly Media, Inc., 2008)

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  24. M. Milakov, Convolutional and fully-connected neural networks C++ framework. http://milakov.github.io/nnForge/

  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

    Google Scholar 

  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

    Google Scholar 

  27. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

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Acknowledgements

The research is supported by Russian Foundation for Basic Research (Grant 15-37-20360).

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Authors and Affiliations

  1. Aylamazyan Program Systems Institute of the Russian Academy of Sciences, Moscow, Russia

    V. P. Fralenko

  2. Federal Research Center “Computer Science and Control”, Russian Academy of Sciences, Moscow, Russia

    R. E. Suvorov & I. A. Tikhomirov

Authors
  1. V. P. Fralenko
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  2. R. E. Suvorov
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  3. I. A. Tikhomirov
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Corresponding author

Correspondence to V. P. Fralenko .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  2. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

  3. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

  5. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

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Fralenko, V.P., Suvorov, R.E., Tikhomirov, I.A. (2018). Automatic Image Classification for Web Content Filtering: New Dataset Evaluation. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_27

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75407-9

  • Online ISBN: 978-3-319-75408-6

  • eBook Packages: EngineeringEngineering (R0)

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