Abstract
The main approach taken to identify pornographic video content is achieved by performing pornography detection on the video content. By extracting features from video key frames and using some common neural network models to recognize the extracted key frame images, a certain accuracy rate can be obtained. However, another key information of video recognition, action information, is ignored, which leads to misclassification of some indistinguishable videos such as sumo wrestling and boxing. A dual-stream convolutional neural network-based pornographic video recognition method is proposed to address this problem. The experimental results show that the dual-stream convolutional neural network effectively improves the recognition rate of indistinguishable pornographic videos.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computer Applications Technology and Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
