Article

Learning user interest for image browsing on small-form-factor devices

Authors:

Wei-Ying MaAuthors Info & Claims

CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 671 - 680

https://doi.org/10.1145/1054972.1055065

Published: 02 April 2005 Publication History

Abstract

Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers.

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Cited By

Chang CTang YYang XChen XIgarashi T(2024)Speed Labeling: Non-stop Scrolling for Fast Image LabelingProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670958(1-10)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670958
Zhang AJiang XZhang BCao X(2020)Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd CountingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/335601916:1s(1-20)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3356019
Georgakoudis GVandierendonck HThoman PSupinski BFahringer TNikolopoulos D(2017)SCALOACM Transactions on Architecture and Code Optimization10.1145/315864314:4(1-25)Online publication date: 18-Dec-2017
https://dl.acm.org/doi/10.1145/3158643
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Index Terms

Learning user interest for image browsing on small-form-factor devices
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2005

928 pages

ISBN:1581139985

DOI:10.1145/1054972

Conference Chairs:
Wendy Kellogg
IBM T. J. Watson Research Center
,
Shumin Zhai
IBM Almaden Research Center
,
General Chair:
Gerrit van der Veer
Vrije Universiteit, The Netherlands
,
Program Chair:
Carolyn Gale
Stanford University, USA

Copyright © 2005 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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Published: 02 April 2005

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CHI05: CHI 2005 Conference on Human Factors in Computing Systems

April 2 - 7, 2005

Oregon, Portland, USA

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CHI '05 Paper Acceptance Rate 93 of 372 submissions, 25%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Chang CTang YYang XChen XIgarashi T(2024)Speed Labeling: Non-stop Scrolling for Fast Image LabelingProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670958(1-10)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670958
Zhang AJiang XZhang BCao X(2020)Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd CountingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/335601916:1s(1-20)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3356019
Georgakoudis GVandierendonck HThoman PSupinski BFahringer TNikolopoulos D(2017)SCALOACM Transactions on Architecture and Code Optimization10.1145/315864314:4(1-25)Online publication date: 18-Dec-2017
https://dl.acm.org/doi/10.1145/3158643
Patil AGovindarajan R(2017)HAShCacheACM Transactions on Architecture and Code Optimization10.1145/315864114:4(1-26)Online publication date: 18-Dec-2017
https://dl.acm.org/doi/10.1145/3158641
Kanakagiri RPanda BMutyam M(2017)MBZipACM Transactions on Architecture and Code Optimization10.1145/315103314:4(1-29)Online publication date: 5-Dec-2017
https://dl.acm.org/doi/10.1145/3151033
Angerd ASintorn EStenström P(2017)A Framework for Automated and Controlled Floating-Point Accuracy Reduction in Graphics Applications on GPUsACM Transactions on Architecture and Code Optimization10.1145/315103214:4(1-25)Online publication date: 5-Dec-2017
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Neill RDrebes APop A(2017)FuseACM Transactions on Architecture and Code Optimization10.1145/314805414:4(1-26)Online publication date: 5-Dec-2017
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Sardashti SWood D(2017)Could Compression Be of General Use? Evaluating Memory Compression across DomainsACM Transactions on Architecture and Code Optimization10.1145/313880514:4(1-24)Online publication date: 5-Dec-2017
https://dl.acm.org/doi/10.1145/3138805
(2017)BibliographyFrontiers of Multimedia Research10.1145/3122865.3122878(315-377)Online publication date: 19-Dec-2017
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Chen KCai WShea RHuang CLiu JLeung VHsu C(2017)Cloud gamingFrontiers of Multimedia Research10.1145/3122865.3122877(287-314)Online publication date: 19-Dec-2017
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