Pornographic images filtering model based on high-level semantic bag-of-visual-words
Lintao Lv, Chengxuan Zhao, Shang Jin, Yuxiang Yang · Journal of Computer Applications · 2011
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.