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Bootstrapping deep feature hierarchy for pornographic image recognition
Kai Li1; Junliang Xing1; Bing Li1; Weiming Hu1,2
2016
Conference NameIEEE International Conference on Image Processing
Pages4423-4427
Conference DateSeptember 25 - 28, 2016
Conference PlaceArizona, USA
AbstractAutomatically recognizing pornographic images from the Web is a vital step to purify Internet environment. Inspired by the rapid developments of deep learning models, we present a deep architecture of convolutional neural network (CNN) for high accuracy pornographic image recognition. The proposed architecture is built upon existing CNNs which accepts input images of different sizes and incorporates features from different hierarchy to perform prediction. To effectively train the model, we propose a two-stage training strategy to learn the model parameters from scratch and end-to-end. During the training procedure, we also employ a hard negative sampling strategy to further reduce the false positive rate of the model. Experimental results on a large dataset demonstrate good performance of the proposed model and the effectiveness of our training strategies, with a considerable improvement over some traditional methods using hand-crafted features and deep learning method using mainstream CNN architecture.
KeywordPornographic Image Recognition Deep Learning Bootstrap
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13481
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
2.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China
Recommended Citation
GB/T 7714
Kai Li,Junliang Xing,Bing Li,et al. Bootstrapping deep feature hierarchy for pornographic image recognition[C],2016:4423-4427.
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