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Personalized photograph ranking and selection system

Authors:

Brian A. Barsky,

Ming OuhyoungAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 211 - 220

https://doi.org/10.1145/1873951.1873963

Published: 25 October 2010 Publication History

Abstract

In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features, such as texture, RGB color, portrait (through face detection), and black-and-white, are included for individual preferences. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account.

The performance of our system in terms of precision-recall diagram and binary classification accuracy (93%) is close to the best results to date for both overall system and individual features. Two personalized ranking user interfaces are provided: one is feature-based and the other is example-based. Although both interfaces are effective in providing personalized preferences, our user study showed that example-based was preferred by twice as many people as feature-based.

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Cited By

Lv PFan JNie XDong WJiang XZhou BXu MXu C(2023)User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement LearningIEEE Transactions on Multimedia10.1109/TMM.2021.313075225(736-749)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2021.3130752
Zhu TLi LChen PWu JYang YLi YGuo Y(2023)Attribute-assisted Multimodal Network for Image Aesthetics Assessment2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00422(2477-2482)Online publication date: Jul-2023
https://doi.org/10.1109/ICME55011.2023.00422
Zhang LShang YLi PLuo HShao L(2022)Community-Aware Photo Quality Evaluation by Deeply Encoding Human PerceptionIEEE Transactions on Cybernetics10.1109/TCYB.2019.293731952:5(3136-3146)Online publication date: May-2022
https://doi.org/10.1109/TCYB.2019.2937319
Show More Cited By

Index Terms

Personalized photograph ranking and selection system
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Copyright © 2010 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: 25 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Lv PFan JNie XDong WJiang XZhou BXu MXu C(2023)User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement LearningIEEE Transactions on Multimedia10.1109/TMM.2021.313075225(736-749)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2021.3130752
Zhu TLi LChen PWu JYang YLi YGuo Y(2023)Attribute-assisted Multimodal Network for Image Aesthetics Assessment2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00422(2477-2482)Online publication date: Jul-2023
https://doi.org/10.1109/ICME55011.2023.00422
Zhang LShang YLi PLuo HShao L(2022)Community-Aware Photo Quality Evaluation by Deeply Encoding Human PerceptionIEEE Transactions on Cybernetics10.1109/TCYB.2019.293731952:5(3136-3146)Online publication date: May-2022
https://doi.org/10.1109/TCYB.2019.2937319
Hara K(2021)Estimating Visual Saliency for Omnidirectional HDR ImagesAnalyzing Future Applications of AI, Sensors, and Robotics in Society10.4018/978-1-7998-3499-1.ch015(249-272)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-3499-1.ch015
Tanaka HNakajima T(2021)A Photocomposition Search System to Improve Your Photo SkillsDesign, User Experience, and Usability: Design for Contemporary Technological Environments10.1007/978-3-030-78227-6_29(396-406)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-78227-6_29
Li YYang CChang Y(2020)Photo Composition with Real-Time RatingSensors10.3390/s2003058220:3(582)Online publication date: 21-Jan-2020
https://doi.org/10.3390/s20030582
Kim WChoi JLee J(2020)Objectivity and Subjectivity in Aesthetic Quality Assessment of Digital PhotographsIEEE Transactions on Affective Computing10.1109/TAFFC.2018.280975211:3(493-506)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TAFFC.2018.2809752
Shu YLi QLiu SXu G(2020)Learning with Privileged Information for Photo Aesthetic AssessmentNeurocomputing10.1016/j.neucom.2020.04.142Online publication date: May-2020
https://doi.org/10.1016/j.neucom.2020.04.142
Kuzovkin DPouli TMeur OCozot RKervec JBouatouch K(2019)Context in Photo AlbumsACM Transactions on Applied Perception10.1145/333361216:2(1-20)Online publication date: 13-Aug-2019
https://dl.acm.org/doi/10.1145/3333612
Constantin MRedi MZen GIonescu B(2019)Computational Understanding of Visual Interestingness Beyond SemanticsACM Computing Surveys10.1145/330129952:2(1-37)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3301299
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