Paper Info
Title
Advertising object in web videos
Abstract
We have witnessed the booming of contextual video advertising in recent years. However, those advertisement systems solely take the metadata into account, such as titles, descriptions and tags. This kind of text-based contextual advertising reveals a number of shortcomings in ads insertion and ads association. In this paper, we present a novel video advertising system called VideoAder. The system leverages the well organized media information from the video corpus for embedding visual content relevant ads into a set of precisely located insertion position. Given a product, we utilize content-based object retrieval technique to identify the relevant ads and their potential embedding positions in the video stream. Then we formulate the ads association as an optimization problem to maximize the total revenue for the system. Specifically, the ''Single-Merge'' and ''Merge'' methods are proposed to tackle the complex query in visual representation. Typical Feature Intensity (TFI) is used to train a classifier to automatically decide which method is more representive. Experimental results demonstrated the accuracy and feasibility of the system.
Year
DOI
Venue
2013
10.1016/j.neucom.2012.04.040
Neurocomputing
Keywords
Field
DocType
web video,advertisement system,insertion position,video stream,ads association,video corpus,contextual video advertising,advertising object,potential embedding position,novel video advertising system,ads insertion,text-based contextual advertising,product
Metadata,Contextual advertising,Embedding,Advertising,Computer science,Classifier (linguistics),Merge (version control),Total revenue,Multimedia,Optimization problem
Journal
Volume
ISSN
Citations 
119,
0925-2312
7
PageRank 
References 
Authors
0.51
25
5
Name
Order
Citations
PageRank
Richang Hong14791176.47
Lin-Xie Tang2332.14
Jun Hu3113.08
Guangda Li445017.15
J. Guo525531.34