Paper Info
Title
Videoader: a video advertising system based on intelligent analysis of visual content
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.
Year
DOI
Venue
2011
10.1145/2043674.2043683
ICIMCS
Keywords
Field
DocType
video advertisement,intelligent analysis,advertisement system,video stream,video corpus,potential embedding position,novel video advertising system,visual content relevant ad,complex query,relevant ad,typical feature intensity,product,intelligence analysis
Computer vision,Metadata,Embedding,Advertising,Context based,Computer science,Video tracking,Artificial intelligence,Classifier (linguistics),Merge (version control),Multimedia
Conference
Citations 
PageRank 
References 
2
0.38
14
Authors
5
Name
Order
Citations
PageRank
Jun Hu1113.08
Guangda Li245017.15
Zhen Lu320.38
Jun Xiao451350.95
Richang Hong54791176.47