Retrieval of Commercials by Semantic Content: The Semiotic Perspective
Multimedia Tools and Applications
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
TV ad video categorization with probabilistic latent concept learning
Proceedings of the international workshop on Workshop on multimedia information retrieval
VideoSense: towards effective online video advertising
Proceedings of the 15th international conference on Multimedia
A generic virtual content insertion system based on visual attention analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Contextual in-image advertising
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Hierarchical clustering-based navigation of image search results
MM '08 Proceedings of the 16th ACM international conference on Multimedia
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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With the proliferation of online media services, video ads are pervasive across various platforms involving internet services and interactive TV services. Existing research efforts such as Google AdSense and MSRA VideoSense/ImageSense have been devoted to the less intrusive insertion of relevant textual or video ads in streams or web pages through text/image/video content analysis whereas the inherent semantics of video ads is much less exploited. In this paper, we propose to link video ads with relevant product/service information across E-commerce websites or portals towards ad recommendation in a cross-media manner. Firstly, we carry out semantic analysis within ad videos in which Frames Marked with Product Images (FMPI) are extracted. Secondly, we link ad videos with relevant ads on the Web by utilizing FMPI to search visually similar Product Images (e.g. appearance or logo) and to collect their accompanying text (brand name, category, description, or other tags) over popular E-commerce websites or portals such as EBay, Amazon, Taobao, etc. We search visually similar product images with Local Sensitive Hashing (LSH) in a Naïve Bayes Near Neighbor classifier. Finally, we may recommend more relevant products/services for ad videos through ranking those matched product images and categorizing useful tags of top ranked ads from the Web. Preliminary experiments have been carried out to demonstrate the idea of linking ad videos with product/service information from the Web.