Knowledge Commons of Institute of Automation,CAS
Bootstrapping deep feature hierarchy for pornographic image recognition | |
Kai Li1; Junliang Xing1; Bing Li1; Weiming Hu1,2 | |
2016 | |
会议名称 | IEEE International Conference on Image Processing |
页码 | 4423-4427 |
会议日期 | September 25 - 28, 2016 |
会议地点 | Arizona, USA |
摘要 | Automatically 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. |
关键词 | Pornographic Image Recognition Deep Learning Bootstrap |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/13481 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICIP16BootstappingDe(245KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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