research-article

ImageSense: Towards contextual image advertising

Authors:

Xian-Sheng Hua,

Shipeng LiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 8, Issue 1

Article No.: 6, Pages 1 - 18

https://doi.org/10.1145/2071396.2071402

Published: 03 February 2012 Publication History

Abstract

The daunting volumes of community-contributed media contents on the Internet have become one of the primary sources for online advertising. However, conventional advertising treats image and video advertising as general text advertising by displaying relevant ads based on the contents of the Web page, without considering the inherent characteristics of visual contents. This article presents a contextual advertising system driven by images, which automatically associates relevant ads with an image rather than the entire text in a Web page and seamlessly inserts the ads in the nonintrusive areas within each individual image. The proposed system, called ImageSense, supports scalable advertising of, from root to node, Web sites, pages, and images. In ImageSense, the ads are selected based on not only textual relevance but also visual similarity, so that the ads yield contextual relevance to both the text in the Web page and the image content. The ad insertion positions are detected based on image salience, as well as face and text detection, to minimize intrusiveness to the user. We evaluate ImageSense on a large-scale real-world images and Web pages, and demonstrate the effectiveness of ImageSense for online image advertising.

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Index Terms

ImageSense: Towards contextual image advertising
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 8, Issue 1

January 2012

149 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2071396

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 03 February 2012

Accepted: 01 August 2010

Revised: 01 July 2010

Received: 01 March 2010

Published in TOMM Volume 8, Issue 1

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Cited By

Fu ZXie HFang SWang YXing MZhang Y(2023)Learning Pixel Affinity Pyramid for Arbitrary-Shaped Text DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/352461719:1s(1-24)Online publication date: 3-Feb-2023
https://dl.acm.org/doi/10.1145/3524617
An SZhou FYang MBai STang CZhu H(2023)Learning an insertion region for advertisement embedding on planesSignal Processing: Image Communication10.1016/j.image.2023.116963116(116963)Online publication date: Aug-2023
https://doi.org/10.1016/j.image.2023.116963
Ye KNazari NHahn JHussain ZZhang MKovashka A(2021)Interpreting the Rhetoric of Visual AdvertisementsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.294744043:4(1308-1323)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TPAMI.2019.2947440
Song XXu BJiang Y(2021)Predicting Content Similarity via Multimodal Modeling for Video-In-Video AdvertisingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297992831:2(569-581)Online publication date: Feb-2021
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Mejía-Trejo J(2021)COVID-19 ads on purchase intention of online consumer behavior as business innovation activityElectronic Commerce Research and Applications10.1016/j.elerap.2021.10108649:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.elerap.2021.101086
Chen XMitra SSwaminathan VHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Metadata Matters in User Engagement PredictionProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401201(1529-1532)Online publication date: 25-Jul-2020
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Chen XNguyen TShen ZKankanhalli MAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)LiveSenseProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3350888(392-400)Online publication date: 15-Oct-2019
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Jhinn WHoong PChua H(2019)Combination of 1D CNN and 2D CNN to Evaluate the Attractiveness of Display Image Advertisement and CTR PredictionSoftware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing10.1007/978-3-030-26428-4_11(157-170)Online publication date: 23-Aug-2019
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