Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
VideoSense: towards effective online video advertising
Proceedings of the 15th international conference on Multimedia
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Contextual video advertising system using scene information inferred from video scripts
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Dynamic captioning: video accessibility enhancement for hearing impairment
Proceedings of the international conference on Multimedia
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Mediapedia: mining web knowledge to construct multimedia encyclopedia
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
In-Image Accessibility Indication
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Advertising object in web videos
Neurocomputing
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Recent years have witnessed the prevalence of context based video advertisement. However, those advertisement systems solely take the metadata into account, such as titles, descriptions and tags. In this paper, we present a novel video advertising system called VideoAder. The system leverages the rich information from the video corpus for embedding visual content relevant ads. Given a product, we utilize content-based object retrieval technique to identify the relevant ads and their potential embedding positions in the video stream. Specifically, the "Single-Merge" and "Merge" methods are proposed to tackle the complex query. Typical Feature Intensity (TFI) is used to train a classifier to automatically deciding which method is better in one situation. Experimental results demonstrated the feasibility of the system.