research-article

Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data

Authors:

Xiaojuan MaAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 2, Issue CSCW

Article No.: 187, Pages 1 - 24

https://doi.org/10.1145/3274456

Published: 01 November 2018 Publication History

Abstract

Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.

References

[1]

2015. The Easter Eggs Hidden In The Names Of Instagram Filters. http://www.reinery29.com/2015/12/99486/instagram-ilters-names. (December 2015).

[2]

2017. Adobe Kuler: Color wheel | Color schemes. https://color.adobe.com/. (September 2017).

[3]

2017. Colourlovers. http://www.colourlovers.com/. (September 2017).

[4]

2018. Google News. https://news.google.com/. (July 2018).

[5]

Saeideh Bakhshi and Eric Gilbert. 2015. Red, purple and pink: The colors of difusion on Pinterest. PloS one 10, 2 (2015), e0117148.

[6]

Saeideh Bakhshi, David Shamma, Lyndon Kennedy, and Eric Gilbert. 2015. Why We Filter Our Photos and How It Impacts Engagement. (2015). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10573/10484

[7]

Jörg Becker, Christian Brelage, Karsten Klose, and Michael Thygs. 2003. Conceptual Modeling of Semantic Navigation Structures: The MoSeNa-approach. In Proceedings of the 5th ACM International Workshop on Web Information and Data Management (WIDM '03). ACM, New York, NY, USA, 118--125.

Digital Library

[8]

Robert Benavente, Francesc Tous, Ramon Baldrich, and Maria Vanrell. 2002. Statistical Modelling of a Colour Naming Space. In European Conference on Colour in Graphics, Imaging and Vision (CGIV'02). Poitiers (France), 406--411. http://www.cat.uab.cat/Public/Publications/2002/BTB2002

[9]

Alessandro Bertoni. 2018. Role and Challenges of Data-Driven Design in the Product Innovation Process. In 16th IFAC Symposium on Information Control Problems in Manufacturing-INCOM 18.

[10]

V. Bychkovsky, S. Paris, E. Chan, and F. Durand. 2011. Learning Photographic Global Tonal Adjustment with a Database of Input/Output Image Pairs. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11). IEEE Computer Society, Washington, DC, USA, 97--104.

Digital Library

[11]

Elena Caoduro. 2014. Photo Filter Apps: Understanding Analogue Nostalgia in the New Media Ecology. Networking Knowledge: Journal of the MeCCSA Postgraduate Network 7, 2 (2014). http://ojs.meccsa.org.uk/index.php/netknow/article/view/338

[12]

José M Cavanillas, Edward Curry, and Wolfgang Wahlster. 2016. New horizons for a data-driven economy: a roadmap for usage and exploitation of big data in Europe. Springer.

Digital Library

[13]

Jason Chuang, Maureen Stone, and Pat Hanrahan. 2008. A probabilistic model of the categorical association between colors. In Color and Imaging Conference, Vol. 2008. Society for Imaging Science and Technology, 6--11.

[14]

Gabriela Csurka, Sandra Skaf, Luca Marchesotti, and Craig Saunders. 2010. Learning moods and emotions from color combinations. In Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing. ACM, 298--305.

Digital Library

[15]

Leah Findlater and Joanna McGrenere. 2007. Evaluating reduced-functionality interfaces according to feature indability and awareness. In IFIP Conference on Human-Computer Interaction. Springer, 592--605.

Digital Library

[16]

L. A. Gatys, A. S. Ecker, and M. Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2414--2423.

[17]

Xue Geng, Hanwang Zhang, Zheng Song, Yang Yang, Huanbo Luan, and Tat-Seng Chua. 2014. One of a kind: User proiling by social curation. In Proceedings of the 22nd ACM international conference on Multimedia. ACM, 567--576.

Digital Library

[18]

Paul Heckbert. 1982. Color Image Quantization for Frame Bufer Display. SIGGRAPH Comput. Graph. 16, 3 (July 1982), 297--307.

Digital Library

[19]

Jefrey Heer and Maureen Stone. 2012. Color Naming Models for Color Selection, Image Editing and Palette Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ACM, New York, NY, USA, 1007--1016.

Digital Library

[20]

Loïc P Heurley, Thibaut Brouillet, Gabrielle Chesnoy, and Denis Brouillet. 2013. Color perception involves color representations irstly at a semantic level and then at a lexical level. Cognitive processing 14, 1 (2013), 19--29.

[21]

Xiaodi Hou and Liqing Zhang. 2007. Color Conceptualization. In Proceedings of the 15th ACM International Conference on Multimedia (MM '07). ACM, New York, NY, USA, 265--268.

Digital Library

[22]

Bernd Huber, Daniel McDuf, Chris Brockett, Michel Galley, and Bill Dolan. 2018. Emotional Dialogue Generation Using Image-Grounded Language Models. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 277, 12 pages.

Digital Library

[23]

Katsuhiko Ishiguro, Akisato Kimura, and Koh Takeuchi. 2012. Towards automatic image understanding and mining via social curation. In Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 906--911.

Digital Library

[24]

Ali Jahanian, Shaiyan Keshvari, S. V. N. Vishwanathan, and Jan P. Allebach. 2017. Colors -- Messengers of Concepts: Visual Design Mining for Learning Color Semantics. ACM Trans. Comput.-Hum. Interact. 24, 1, Article 2 (Jan. 2017), 39 pages.

Digital Library

[25]

EunJin Kim and Hyeon-Jeong Suk. 2017. Thoughts and Tools for Crafting Colors: Implications from Designers' Behavior. In Proceedings of the 2017 Conference on Designing Interactive Systems (DIS '17). ACM, New York, NY, USA, 321--331.

Digital Library

[26]

Akisato Kimura, Katsuhiko Ishiguro, Makoto Yamada, Alejandro Marcos Alvarez, Kaori Kataoka, and Kazuhiko Murasaki. 2013. Image context discovery from socially curated contents. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 565--568.

Digital Library

[27]

Shigenobu Kobayashi. 1981. The aim and method of the color image scale. Color Research & Application 6, 2 (1981), 93--107.

[28]

Lauren I Labrecque, Vanessa M Patrick, and George R Milne. 2013. The marketers' prismatic palette: A review of color research and future directions. Psychology & Marketing 30, 2 (2013), 187--202.

[29]

Pierre-Yves Lafont, Zhile Ren, Xiaofeng Tao, Chao Qian, and James Hays. 2014. Transient Attributes for Highlevel Understanding and Editing of Outdoor Scenes. ACM Trans. Graph. 33, 4, Article 149 (July 2014), 11 pages.

Digital Library

[30]

Hyojin Lee, Xiaoyan Deng, H Rao Unnava, and Kentaro Fujita. 2014. Monochrome Forests and Colorful Trees: The Efect of Black-and-White versus Color Imagery on Construal Level. 41, 4 (2014), 1015--1032. http://www.jstor.org/stable/10.1086/678392

[31]

J. Y. Lee, K. Sunkavalli, Z. Lin, X. Shen, and I. S. Kweon. 2016. Automatic Content-Aware Color and Tone Stylization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2470--2478.

[32]

Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, and Jefrey Heer. 2013. Selecting Semantically-Resonant Colors for Data Visualization. In Computer Graphics Forum, Vol. 32. Wiley Online Library, 401--410.

Digital Library

[33]

Sharon Lin and Pat Hanrahan. 2013. Modeling How People Extract Color Themes from Images. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). ACM, New York, NY, USA, 3101--3110.

Digital Library

[34]

A. Lindner and S. Süsstrunk. 2015. Semantic-Improved Color Imaging Applications: It Is All About Context. IEEE Transactions on Multimedia 17, 5 (May 2015), 700--710.

[35]

Yiming Liu, Michael Cohen, Matt Uyttendaele, and Szymon Rusinkiewicz. 2014. AutoStyle: Automatic Style Transfer from Image Collections to Users' Images. In Proceedings of the 25th Eurographics Symposium on Rendering (EGSR '14). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 21--31.

Digital Library

[36]

C. M. MacLeod. 1991. Half a century of research on the Stroop efect: an integrative review. Psychological Bulletin 109, 2 (March 1991), 163--203.

[37]

Winter Mason and Siddharth Suri. 2012. Conducting behavioral research on Amazon's Mechanical Turk. Behavior research methods 44, 1 (2012), 1--23.

[38]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jefrey Dean. 2013. Eicient Estimation of Word Representations in Vector Space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781

[39]

George A. Miller. 1995. WordNet: A Lexical Database for English. Commun. ACM 38, 11 (Nov. 1995), 39--41.

Digital Library

[40]

Nathan Moroney. 2003. Unconstrained web-based color naming experiment. In Color imaging VIII: Processing, hardcopy, and applications, Vol. 5008. International Society for Optics and Photonics, 36--47.

[41]

Naila Murray, Sandra Skaf, Luca Marchesotti, and Florent Perronnin. 2012. Toward automatic and lexible concept transfer. Computers & Graphics 36, 6 (2012), 622--634.

Digital Library

[42]

Dimitris Mylonas, Lindsay MacDonald, and Sophie Wuerger. 2010. Towards an online color naming model. In Color and Imaging Conference, Vol. 2010. Society for Imaging Science and Technology, 140--144.

[43]

Peter O'Donovan, Aseem Agarwala, and Aaron Hertzmann. 2011. Color Compatibility from Large Datasets. ACM Trans. Graph. 30, 4, Article 63 (July 2011), 12 pages.

Digital Library

[44]

Peter O'Donovan, Janis Libeks, Aseem Agarwala, and Aaron Hertzmann. 2014. Exploratory Font Selection Using Crowdsourced Attributes. ACM Trans. Graph. 33, 4, Article 92 (July 2014), 9 pages.

Digital Library

[45]

Carlos Padoa, Daniel Schneider, Jano Moreira De Souza, and Sergio Palma J Medeiros. 2015. Investigating social curation websites: A crowd computing perspective. In Computer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on. IEEE, 253--258.

[46]

Stephen E Palmer and Karen B Schloss. 2010. An ecological valence theory of human color preference. Proceedings of the National Academy of Sciences 107, 19 (2010), 8877--8882.

[47]

Zoran Pecenovió, Minh N. Do, Martin Vetterli, and Pearl Pu. 2000. Integrated Browsing and Searching of Large Image Collections. Springer Berlin Heidelberg, Berlin, Heidelberg, 279--289.

[48]

Yilang Peng. 2017. Time Travel with One Click: Efects of Digital Filters on Perceptions of Photographs. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 6000--6011.

Digital Library

[49]

Judith Redi and Isabel Povoa. 2014. Crowdsourcing for Rating Image Aesthetic Appeal: Better a Paid or a Volunteer Crowd?. In Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM '14). ACM, New York, NY, USA, 25--30.

Digital Library

[50]

Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas. 2000. The earth mover's distance as a metric for image retrieval. International journal of computer vision 40, 2 (2000), 99--121.

Digital Library

[51]

Vidya Setlur and Maureen C Stone. 2016. A linguistic approach to categorical color assignment for data visualization. IEEE transactions on visualization and computer graphics 22, 1 (2016), 698--707.

[52]

Martin Solli and Reiner Lenz. 2010. Color semantics for image indexing. In Conference on Colour in Graphics, Imaging, and Vision, Vol. 2010. Society for Imaging Science and Technology, 353--358.

[53]

W. T. Sun, T. H. Chao, Y. H. Kuo, and W. H. Hsu. 2017. Photo Filter Recommendation by Category-Aware Aesthetic Learning. IEEE Transactions on Multimedia 19, 8 (Aug 2017), 1870--1880.

[54]

Baoyuan Wang, Yizhou Yu, Tien-Tsin Wong, Chun Chen, and Ying-Qing Xu. 2010. Data-driven Image Color Theme Enhancement. ACM Trans. Graph. 29, 6, Article 146 (Dec. 2010), 10 pages.

Digital Library

[55]

Ka-Ping Yee, Kirsten Swearingen, Kevin Li, and Marti Hearst. 2003. Faceted Metadata for Image Search and Browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03). ACM, New York, NY, USA, 401--408.

Digital Library

Cited By

Crain PLee JYen YKim JAiello ABailey B(2022)Visualizing Topics and Opinions Helps Students Interpret Large Collections of Peer Feedback for Creative ProjectsACM Transactions on Computer-Human Interaction10.1145/357181730:3(1-30)Online publication date: 20-Dec-2022
https://dl.acm.org/doi/10.1145/3571817
Guo QZhou SWu YPeng ZMa X(2022)Understanding and Modeling Viewers’ First Impressions with Images in Online Medical Crowdfunding CampaignsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501830(1-20)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501830
Wu ZXu QLiu YPeng ZXu YMa X(2021)Exploring Designers’ Practice of Online Example Management for Supporting Mobile UI DesignProceedings of the 23rd International Conference on Mobile Human-Computer Interaction10.1145/3447526.3472048(1-12)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3447526.3472048
Show More Cited By

Index Terms

Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods

Recommendations

Novel color demosaicking for noisy color filter array data

Single sensor digital color still/video cameras use color demosaicking to reproduce full color images from color filter array (CFA) data. The quality of interpolated image will be degraded due to the sensor noise introduced during the image capture ...
New Fuzzy Color Median Filter
Abstract
Median filters are widely used in color image processing. In this paper a new fuzzy median filter is introduced, suitable for the removal of impulse noise in color images. In order to apply a median operator, some kind of ordering of vectors is ...
Data-driven image color theme enhancement

It is often important for designers and photographers to convey or enhance desired color themes in their work. A color theme is typically defined as a template of colors and an associated verbal description. This paper presents a data-driven method for ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 2, Issue CSCW

November 2018

4104 pages

EISSN:2573-0142

DOI:10.1145/3290265

Editors:
Karrie Karahalios
University of Illinois & Adobe
,
Andrés Monroy-Hernández
Snap Inc.
,
Airi Lampinen
Stockholm University
,
Geraldine Fitzpatrick
Vienna University of Technology

Issue’s Table of Contents

Copyright © 2018 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2018

Published in PACMHCI Volume 2, Issue CSCW

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

the Hong Kong Research Grants Council
Hong Kong ITF Grant

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
337
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)2

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Crain PLee JYen YKim JAiello ABailey B(2022)Visualizing Topics and Opinions Helps Students Interpret Large Collections of Peer Feedback for Creative ProjectsACM Transactions on Computer-Human Interaction10.1145/357181730:3(1-30)Online publication date: 20-Dec-2022
https://dl.acm.org/doi/10.1145/3571817
Guo QZhou SWu YPeng ZMa X(2022)Understanding and Modeling Viewers’ First Impressions with Images in Online Medical Crowdfunding CampaignsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501830(1-20)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501830
Wu ZXu QLiu YPeng ZXu YMa X(2021)Exploring Designers’ Practice of Online Example Management for Supporting Mobile UI DesignProceedings of the 23rd International Conference on Mobile Human-Computer Interaction10.1145/3447526.3472048(1-12)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3447526.3472048
Kang YSun ZWang SHuang ZWu ZMa XKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)MetaMap: Supporting Visual Metaphor Ideation through Multi-dimensional Example-based ExplorationProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445325(1-15)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445325
Chu WFan Y(2019)Photo Filter Classification and Filter Recommendation without Much Manual Labeling2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP.2019.8901831(1-6)Online publication date: Sep-2019
https://doi.org/10.1109/MMSP.2019.8901831

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents