基于SVM的裸体图像检测

Xin-Lu Wang, Xiao-juan Li, Xiao-bo Liu
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引用次数: 6

摘要

在网络上,裸照引发了大量社会问题的传播,如何准确识别裸照是一个急需解决的问题。因此,我们将图像处理方法与支持向量机(SVM)相结合,研究了一种新的增强裸照识别方法,即结合人脸检测模型、肤色模型和纹理模型,提取6个裸照特征向量。此外,通过实验确定了支持向量机的一些重要因素,如训练集、核函数和代价。实验结果表明,基于支持向量机的裸照检测分类在提高预测精度的同时效果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nude Image Detection Based on SVM
On the Internet, the nude images caused the spread of a large number of social problems, how to identify the nude image accurately is a problem needing to be solved urgently. Therefore, we integrate both image processing method and support vector machines (SVM), this paper studies a new and enhanced approach on recognition of nude image, namely, combine a face detection model, skin color model and texture model, extract six nude image feature vectors. Additionally, some important factors of SVM are fixed by experiments, such as the training set, kernel function and the cost. The experimental results demonstrate that performing SVM-based nude image detective classification more effective in that it improves the prediction accuracies at the same time.
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