Object-Level Video Advertising: An Optimization Framework

In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are i...

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Bibliographic Details
Published inIEEE transactions on industrial informatics Vol. 13; no. 2; pp. 520 - 531
Main Authors Haijun Zhang, Xiong Cao, Ho, John K. L., Chow, Tommy W. S.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Advertising
Artificial neural networks
Content based
Feature extraction
Feature recognition
Genetic algorithms
Heuristic methods
in-video ads
Informatics
Neural networks
object level
Optimization
Receivers
video advertising
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Summary:In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2016.2605629