research-article

Modeling Image Appeal Based on Crowd Preferences for Automated Person-Centric Collage Creation

Authors:

Vassilios Vonikakis,

Ramanathan Subramanian,

Stefan WinklerAuthors Info & Claims

CrowdMM '14: Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia

Pages 9 - 15

https://doi.org/10.1145/2660114.2660126

Published: 07 November 2014 Publication History

Abstract

This paper attempts to model IA in personal photo collections through a user-centric perspective. To understand how users deemed an image as being more/less appealing, an extensive crowdsourcing experiment was conducted with 350 workers and five different albums. The significant variance in selection probabilities for the most and least appealing images indicated that images were not selected randomly, and there were underlying factors that influenced some images to be selected more often than others. We then employed nine low level image attributes to model the image selection process, and trained SVRs which could adequately predict image selections for the album-specific conditions. However, a generic SVR failed to model the selection patterns as adequately as the album-specific SVRs suggesting that context greatly influences the categorization of what is more and less appealing. Experimental results demonstrate that our approach is promising. However, more attributes (related to image semantics) are needed to accurately model image selection characteristics.

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Cited By

Oppenlaender JAbbas TGadiraju U(2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641023
Li HZhuang YGe YLou T(2023)Towards Effective Crowd-Assisted Similarity Retrieval of Large Cursive Chinese Calligraphic Character ImagesProceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things10.1145/3603781.3603853(403-409)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3603781.3603853
Prietch SPaim PMiranda FNogueira CLeite V(2022)User Interface Evaluation of Design Alternatives of the Emotion-Libras—An Emotional Self-Report Instrument for Sign Language UsersInteracting with Computers10.1093/iwc/iwac01835:2(142-152)Online publication date: 23-Jun-2022
https://doi.org/10.1093/iwc/iwac018
Show More Cited By

Index Terms

Modeling Image Appeal Based on Crowd Preferences for Automated Person-Centric Collage Creation
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
  2. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

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Published In

cover image ACM Conferences

CrowdMM '14: Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia

November 2014

84 pages

ISBN:9781450331289

DOI:10.1145/2660114

General Chairs:
Judith Redi
Delft University of Technology, The Netherlands
,
Mathias Lux
Klagenfurt University, Austria

Copyright © 2014 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2014

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Agency for Science, Technology and Research

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MM '14

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SIGMM

MM '14: 2014 ACM Multimedia Conference

November 7, 2014

Florida, Orlando, USA

Acceptance Rates

CrowdMM '14 Paper Acceptance Rate 8 of 26 submissions, 31%;

Overall Acceptance Rate 16 of 42 submissions, 38%

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Cited By

Oppenlaender JAbbas TGadiraju U(2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641023
Li HZhuang YGe YLou T(2023)Towards Effective Crowd-Assisted Similarity Retrieval of Large Cursive Chinese Calligraphic Character ImagesProceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things10.1145/3603781.3603853(403-409)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3603781.3603853
Prietch SPaim PMiranda FNogueira CLeite V(2022)User Interface Evaluation of Design Alternatives of the Emotion-Libras—An Emotional Self-Report Instrument for Sign Language UsersInteracting with Computers10.1093/iwc/iwac01835:2(142-152)Online publication date: 23-Jun-2022
https://doi.org/10.1093/iwc/iwac018
Jiang NZhuang YChiu D(2020)Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systemsMultimedia Tools and Applications10.1007/s11042-020-08755-3Online publication date: 31-Mar-2020
https://doi.org/10.1007/s11042-020-08755-3
Vonikakis VSubramanian RArnfred JWinkler S(2017)A Probabilistic Approach to People-Centric Photo Selection and SequencingIEEE Transactions on Multimedia10.1109/TMM.2017.269985919:11(2609-2624)Online publication date: Nov-2017
https://doi.org/10.1109/TMM.2017.2699859
Saad MNicholas DMcKnight PQuartuccio JJaladi RCorriveau P(2016)Subtle consumer-photo quality evaluation2016 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2016.7533066(3778-3782)Online publication date: Sep-2016
https://doi.org/10.1109/ICIP.2016.7533066
Vonikakis VSubramanian RWinkler S(2016)Shaping datasets: Optimal data selection for specific target distributions across dimensions2016 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2016.7533061(3753-3757)Online publication date: Sep-2016
https://doi.org/10.1109/ICIP.2016.7533061

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