Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Optimization-based automated home video editing system
IEEE Transactions on Circuits and Systems for Video Technology
Characterizing use and quality of textual attributes in Web 2.0 applications
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
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This demonstration presents a novel contextual advertising platform for online video service, called VideoSense. Unlike most current video-oriented sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Given an online video, VideoSense is able to detect a set of candidate ad insertion points based on content analysis, select a list of relevant candidate ads ranked according to textual relevance, and find the best match between insertion points and ads which maximizes the overall multimodal relevance. The effectiveness of VideoSense supporting contextually relevant and less intrusive advertising is validated by the user studies conducted on a variety of online video documents.