Please use this identifier to cite or link to this item:
|Title:||Advertising object in web videos|
|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.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 21, 2018
WEB OF SCIENCETM
checked on Jun 18, 2018
checked on Jun 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.