VideoAder: A video advertising system based on intelligent analysis of visual content

Jun Hu; Guangda Li; Zhen Lu; Jun Xiao; Richang Hong

doi:10.1145/2043674.2043683

VideoAder: A video advertising system based on intelligent analysis of visual content

Jun Hu, Guangda Li, Zhen Lu, Jun Xiao, Richang Hong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

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. © 2011 ACM.

Original language	English
Title of host publication	ACM International Conference Proceeding Series
Pages	30-33
DOIs	https://doi.org/10.1145/2043674.2043683
Publication status	Published - 2011
Externally published	Yes
Event	3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011 - Chengdu, China Duration: 5 Aug 2011 → 7 Aug 2011

Conference

Conference	3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011
Place	China
City	Chengdu
Period	5/08/11 → 7/08/11

Research Keywords

product
video advertising
visual relevance

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10.1145/2043674.2043683

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@inproceedings{2a37a94422474762bcf491dbb1c9308b,

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. {\textcopyright} 2011 ACM.",

keywords = "product, video advertising, visual relevance",

author = "Jun Hu and Guangda Li and Zhen Lu and Jun Xiao and Richang Hong",

year = "2011",

doi = "10.1145/2043674.2043683",

language = "English",

isbn = "9781450309189",

pages = "30--33",

booktitle = "ACM International Conference Proceeding Series",

note = "3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011 ; Conference date: 05-08-2011 Through 07-08-2011",

}

TY - GEN

T1 - VideoAder

T2 - 3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011

AU - Hu, Jun

AU - Li, Guangda

AU - Lu, Zhen

AU - Xiao, Jun

AU - Hong, Richang

PY - 2011

Y1 - 2011

N2 - 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. © 2011 ACM.

AB - 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. © 2011 ACM.

KW - product

KW - video advertising

KW - visual relevance

UR - http://www.scopus.com/inward/record.url?scp=80755187769&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80755187769&origin=recordpage

U2 - 10.1145/2043674.2043683

DO - 10.1145/2043674.2043683

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781450309189

SP - 30

EP - 33

BT - ACM International Conference Proceeding Series

Y2 - 5 August 2011 through 7 August 2011

ER -

VideoAder: A video advertising system based on intelligent analysis of visual content

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