Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2012.04.040
Title: Advertising object in web videos
Authors: Hong, R.
Tang, L.
Hu, J.
Li, G. 
Jiang, J.-G.
Keywords: Product
Video advertising
Visual relevance
Issue Date: 11-Jul-2013
Citation: Hong, R., Tang, L., Hu, J., Li, G., Jiang, J.-G. (2013-07-11). Advertising object in web videos. Neurocomputing 119 : 118-124. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2012.04.040
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. © 2013 Elsevier B.V.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/77812
ISSN: 09252312
DOI: 10.1016/j.neucom.2012.04.040
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

8
checked on Oct 15, 2018

WEB OF SCIENCETM
Citations

7
checked on Oct 8, 2018

Page view(s)

83
checked on Oct 5, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.