Along with the ever-growing Web, the Video Social Networking Services (VSNS), which integrates the functions of video sharing websites and social networking services, have become popular Web applications and provided convenience for the spreadof objectionable videos. An efficient Web content filtering tool is necessary for the VSNS to protect children from accessing the objectionable content. By introducing visual perception and social computing theories and combining pattern recognition technique, this project aims to filter the objectionable videos and analyze the public video sentiment in the VSNS. The research content includes: (1) Pronographic video recognition under complex illumination conditions based on multimodal fusion. (2) Horror video recognition based on Web context perception. (3) Public video sentiment analysis for VSNS. (4)Potential objectionable video recognition based cross-media sentiment analysis for VSNS. The objective of this project is to integrate the human factors behind the VSNS into Web content security research and mine the underlying relationship between vitual society and real society to improve the existing Web content security system. This research not only promotes related research fields, but also is significant for many practical applications.
