Current pornographic images filtering algorithms have some shortcomings, such as high false positive rate toward the bikinis images and insufficiency when filtering pornographic images with pornographic actions. The paper proposed a new pornographic image filtering model based on High-level Semantic Bag-of-Visual-Words (BoVW). Firstly, local feature points in sex scene were detected using the Speeded-Up Robust Features (SURF) algorithm and then high-level semantic dictionary was constructed by fusing the context of the visual vocabularies and spatial-related high-level semantic features of pornographic images. The experimental results show that the model has an accuracy up to 87.6％ when testing the multiperson pornographic images, which is significantly higher than the existing pornographic images filtering algorithm based on BoVW.%针对目前色情图像过滤算法对比基尼图像和类肤色图像误检率过高,且不能有效过滤带有淫秽动作的多人色情图像的缺点,提出一种基于高层语义视觉词袋的色情图像过滤模型.该模型首先通过改进的SURF算法提取色情场景局部特征点,然后融合视觉单词的上下文和空间相关高层语义特征,从而构建色情图像的高层语义词典.实验结果表明,该模型检测带有淫秽动作的多人色情图像准确率可达87.6%,明显高于现有的视觉词袋色情图像过滤算法.
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