摘要
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。
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 multi-person pornographic images,which is significantly higher than the existing pornographic images filtering algorithm based on BoVW.
出处
《计算机应用》
CSCD
北大核心
2011年第7期1847-1849,共3页
journal of Computer Applications
基金
陕西省自然科学研究计划项目(2010JK7402010JM8019)
西安理工大学学科联合研究项目(102-210914)
关键词
色情图像
过滤
图像高层语义
语义树
视觉词袋
鲁棒特征加速
Pornographic image
filtering
image high-level semantics
semantic tree
Bag-of-Visual-Words（BoVW）
Speeded-Up Robust Features（SURF）
