Lizhi Huang
ABSTRACT
With1 the development of current Internet era, the exchange of data information is becoming more frequent and the spread of erotic images is becoming easier. Under these circumstances, it becomes even more important to identify and classify the images. Deep learning has been widely used in the field of image recognition because of its great advantage in automatically extracting features. However, in the case of small amount of data, it is easy to cause over-fitting of training data. Based on current situation, we propose a new erotic image recognition model. This model adopts Bagging Integrated Convolutional Neural Network and combines traditional Color Features-Histogram of Color based on the depth features. While improving the recognition accuracy, it also increases the sensitivity of the model to the color of the picture. Result of the experiment shows that, when identifying and classifying images in the NPDI data sets, the accuracy of the proposed model reaches 99.31%, which is 2.67% higher than that of the Convolutional Neural Network model, and it has a favorable classification recognition effect.
- M. M. Fleck, D. A. Forsyth, and C. Bregler. 1996. Finding Naked People. European Conference on Computer Vision, 1065, 593--602. Springer-Verlag. Google Scholar
Digital Library
- W. Tang,. and Z. Qu. 2013. An Erotic Image Recognition Method Based on Multi-source Information Fusion. Fifth International Conference on Multimedia Information NETWORKING and Security, 501--506. IEEE Computer Society. Google ScholarDigital Library
- Y. Yang and N. Cheng. 2012. Research of skin color identification based on online update histogram. Computer Engineering & Applications, 48(4), 204--206.Google Scholar
- C. Srisaan. 2016. A classification of internet pornographic images. International Journal of Electronic Commerce Studies, 7(1), 95--104.Google ScholarCross Ref
- J. A. M. Basilio, G. A. Torres, G. S. Pérez, and L. K. T. Medina. 2011. Explicit content image detection. Signal & Image Processing, 1(2), 47--58.Google Scholar
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, 60, 1097--1105. Curran Associates Inc. Google ScholarDigital Library
- K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. Computer Science.Google Scholar
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going deeper with convolutions. IEEE conference on computer vision and pattern recognition, 1--9. IEEE.Google Scholar
- K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770--778. IEEE Computer Society.Google Scholar
- M. Moustafa. 2015. Applying deep learning to classify pornographic images and videos.Google Scholar
- W. Zhao, J. Zheng, A. Liu, Y. Li, and H. Li. 2016. The detecting algorithm of pornographic image based on deep learning and model cascade. Journal of Information Security Research.Google Scholar
- M. Yu, P. Yang, and Y. Wang. 2018. Pornographic image detection based on convolutional neural network. Computer Applications & Software.Google Scholar
- F. Hou and X. Jiang. 2013. Research on the color feature extraction method of the image retrieval. Journal of Communication University of China, 421--424.Google Scholar
- C. Yang, Q. Ren, C. Zhang, Z. Zhou, Q. Li, and L. Qiu. 2017. Research on image feature recognition based on convolution neural network. China Computer & Communication.Google Scholar
- O. Abdel-Hamid, A. R. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu. 2014. Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio Speech & Language Processing, 22(10), 1533--1545. Google ScholarDigital Library
- Q. Zhang, Y. Liu, Z. Wang, J. Pan, and Y. Yan. 2014. The application of convolutional neural network in speech recognition. Journal of Network New Media.Google Scholar
- J. Jin, K. Fu, and C. Zhang. 2014. Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1991--2000.Google ScholarCross Ref
- S. Duffner, S. Berlemont, G. Lefebvre, and C. Garcia. 2014. 3D gesture classification with convolutional neural networks. IEEE International Conference on Acoustics, Speech and Signal Processing, 5432--5436. IEEE.Google Scholar
- S. Li, B. Yu, W. Wu, S. Su, and R. Ji. 2015. Feature learning based on sae--pca network for human gesture recognition in rgbd images. Neurocomputing, 151(151), 565--573.Google ScholarCross Ref
- P. Bühlmann. 2012. Bagging, Boosting and Ensemble Methods. Handbook of Computational Statistics, 985--1022. Springer Berlin Heidelberg.Google Scholar
Index Terms
- Erotic Image Recognition Method of Bagging Integrated Convolutional Neural Network
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