Please use this identifier to cite or link to this item: https://doi.org/10.1145/2043674.2043683
Title: VideoAder: A video advertising system based on intelligent analysis of visual content
Authors: Hu, J.
Li, G. 
Lu, Z. 
Xiao, J.
Hong, R.
Keywords: product
video advertising
visual relevance
Issue Date: 2011
Source: Hu, J.,Li, G.,Lu, Z.,Xiao, J.,Hong, R. (2011). VideoAder: A video advertising system based on intelligent analysis of visual content. ACM International Conference Proceeding Series : 30-33. ScholarBank@NUS Repository. https://doi.org/10.1145/2043674.2043683
Abstract: 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. © 2011 ACM.
Source Title: ACM International Conference Proceeding Series
URI: http://scholarbank.nus.edu.sg/handle/10635/40868
ISBN: 9781450309189
DOI: 10.1145/2043674.2043683
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