demonstration

Aesthetic Evaluation and Guidance for Mobile Photography

Authors:

Xin JinAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 2780 - 2782

https://doi.org/10.1145/3474085.3478557

Published: 17 October 2021 Publication History

Abstract

Nowadays, almost everyone can shoot photos using smart phones. However, not everyone can take good photos. We propose to use computational aesthetics to automatically teach people without photography training to take excellent photos. We present Aesthetic Dashboard: a system of rich aesthetic evaluation and guidance for mobile photography. We take 2 most used types of photos: landscapes and portraits into consideration. When people take photos in the preview mode, for landscapes, we show the overall aesthetic score and scores of 3 basic attributes: light, composition and color usage. Meanwhile, the matching scores of the 3 basic attributes of current preview to typical templates are shown, which can help users to adjust 3 basic attributes accordingly. For portraits, besides the above basic attributes, the facial appearance, the guidance of face light, body pose and the garment color are also shown to the users. This is the first system that can teach mobile users to shoot good photos in the form of aesthetic dashboard, through which, users can adjust several aesthetic attributes to take good photos easily.

Supplementary Material

MP4 File (de3216.mp4)

Supplemental video

Download
80.99 MB

MP4 File (202109301627.mp4)

Description video about demo

Download
60.13 MB

References

[1]

Kuang-Yu Chang, Kung-Hung Lu, and Chu-Song Chen. 2017. Aesthetic Critiques Generation for Photos. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. IEEE Computer Society, 3534--3543. https://doi.org/10.1109/ICCV.2017.380

[2]

Daniel Cohen-Or, Olga Sorkine, Ran Gal, Tommer Leyvand, and Ying-Qing Xu. 2006. Color harmonization. ACM Trans. Graph., Vol. 25, 3 (2006), 624--630. https://doi.org/10.1145/1141911.1141933

Digital Library

[3]

Qi Han, Kai Zhao, Jun Xu, and Ming-Ming Cheng. 2020. Deep Hough Transform for Semantic Line Detection. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX (Lecture Notes in Computer Science, Vol. 12354), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 249--265. https://doi.org/10.1007/978-3-030-58545-7_15

[4]

Xin Jin, Le Wu, Xiaodong Li, Siyu Chen, Siwei Peng, Jingying Chi, Shiming Ge, Chenggen Song, and Geng Zhao. 2018. Predicting Aesthetic Score Distribution Through Cumulative Jensen-Shannon Divergence. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2-7, 2018. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16074

[5]

Xin Jin, Le Wu, Geng Zhao, Xiaodong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, and Xinghui Zhou. 2019. Aesthetic Attributes Assessment of Images. In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21-25, 2019, Laurent Amsaleg, Benoit Huet, Martha A. Larson, Guillaume Gravier, Hayley Hung, Chong-Wah Ngo, and Wei Tsang Ooi (Eds.). ACM, 311--319. https://doi.org/10.1145/3343031.3350970

Digital Library

[6]

Chen Kang, Giuseppe Valenzise, and Frédéric Dufaux. 2020. EVA: An Explainable Visual Aesthetics Dataset. In Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends (Seattle, WA, USA) (ATQAM/MAST'20). Association for Computing Machinery, New York, NY, USA, 5--13. https://doi.org/10.1145/3423268.3423590

Digital Library

[7]

Minju Kim and Jungjin Lee. 2019. PicMe: Interactive Visual Guidance for Taking Requested Photo Composition. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, Stephen A. Brewster, Geraldine Fitzpatrick, Anna L. Cox, and Vassilis Kostakos (Eds.). ACM, 395. https://doi.org/10.1145/3290605.3300625

Digital Library

[8]

Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, and Charless Fowlkes. 2016. Photo Aesthetics Ranking Network with Attributes and Content Adaptation. In ECCV.

[9]

Qi Kuang, Xin Jin, Qinping Zhao, and Bin Zhou. 2020. Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment. IEEE Trans. Multim., Vol. 22, 10 (2020), 2623--2634. https://doi.org/10.1109/TMM.2019.2960656

[10]

Jean-François Lalonde, Alexei A. Efros, and Srinivasa G. Narasimhan. 2012. Estimating the Natural Illumination Conditions from a Single Outdoor Image. Int. J. Comput. Vis., Vol. 98, 2 (2012), 123--145. https://doi.org/10.1007/s11263-011-0501-8

Digital Library

[11]

L. Liang, L. Lin, L. Jin, D. Xie, and M. Li. 2018. SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction. In 2018 24th International Conference on Pattern Recognition (ICPR).

[12]

Jiangjiang Liu, Qibin Hou, and Ming-Ming Cheng. 2020. Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge, and Skeleton. IEEE Trans. Image Process., Vol. 29 (2020), 8652--8667. https://doi.org/10.1109/TIP.2020.3017352

Digital Library

[13]

Yogesh Singh Rawat and Mohan S. Kankanhalli. 2017. ClickSmart: A Context-Aware Viewpoint Recommendation System for Mobile Photography. IEEE Trans. Circuits Syst. Video Technol., Vol. 27, 1 (2017), 149--158. https://doi.org/10.1109/TCSVT.2016.2555658

Digital Library

[14]

Samsung. [n.d.]. How to use Shot suggestions on the Galaxy S10. [EB/OL]. https://www.samsung.com/levant/support/mobile-devices/how-to-use-shot-suggestions/Accessed September 22, 2020.

[15]

Yu-Chuan Su, Raviteja Vemulapalli, Ben Weiss, Chun-Te Chu, Philip Andrew Mansfield, Lior Shapira, and Colvin Pitts. 2021. Camera View Adjustment Prediction for Improving Image Composition. CoRR, Vol. abs/2104.07608 (2021). arxiv: 2104.07608 https://arxiv.org/abs/2104.07608

[16]

Mingxing Tan and Quoc V. Le. 2019. Efficient Net: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 6105--6114. http://proceedings.mlr.press/v97/tan19a.html

[17]

Mingxing Tan, Ruoming Pang, and Quoc V. Le. 2020. EfficientDet: Scalable and Efficient Object Detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. IEEE, 10778--10787. https://doi.org/10.1109/CVPR42600.2020.01079

[18]

Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu. 2020. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. IEEE, 11531--11539. https://doi.org/10.1109/CVPR42600.2020.01155

[19]

Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. 2019. Detectron2. https://github.com/facebookresearch/detectron2.

[20]

Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, and David Jacobs. 2019. Deep Single-Image Portrait Relighting. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. IEEE, 7193--7201. https://doi.org/10.1109/ICCV.2019.00729

Cited By

Jin XZhang LJiang LLi DCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Unlimited Vision: Professional Composition by YourselfProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685001(11264-11266)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3685001
Xie RMing AHe SXiao YMa HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)"Special Relativity" of Image Aesthetics Assessment: a Preliminary Empirical PerspectiveProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681172(2554-2563)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681172
Huang JGong YShi YZhang XZhang JYin Y(2024)Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo SelectionIEEE Transactions on Multimedia10.1109/TMM.2024.338246926(8758-8770)Online publication date: 2-Apr-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3382469
Show More Cited By

Index Terms

Aesthetic Evaluation and Guidance for Mobile Photography
1. Applied computing
  1. Arts and humanities
    1. Media arts

Recommendations

Unlimited Vision: Professional Composition by Yourself
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

This paper introduces a novel method for enhancing image composition guidance in photography. It utilizes advanced composition rules to guide a Real-Time Detection Transformer (RT-DETR) model in predicting aesthetically pleasing compositions for ...
Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry
Abstract
With the development of technology, the quality of AI-generated text has improved. This is relevant in the AI art field, where AI generates literature or poetry that is appreciated. This study compared human-made and AI-generated haiku ...
Highlights
- Human-made and AI-generated poetry were examined using haiku poetry.
- The beauty ...
Photography for Beginners & Photoshop Lightroom: Master Photography & Photoshop Lightroom Tips in 24 Hours or Less!

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

Check for updates

Author Tags

Qualifiers

Demonstration

Funding Sources

Beijing Natural Science Foundation
Advanced Discipline Construction Project of Beijing Universities
National Natural Science Foundation of China

Conference

MM '21

Sponsor:

SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
402
Total Downloads

Downloads (Last 12 months)75
Downloads (Last 6 weeks)5

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jin XZhang LJiang LLi DCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Unlimited Vision: Professional Composition by YourselfProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685001(11264-11266)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3685001
Xie RMing AHe SXiao YMa HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)"Special Relativity" of Image Aesthetics Assessment: a Preliminary Empirical PerspectiveProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681172(2554-2563)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681172
Huang JGong YShi YZhang XZhang JYin Y(2024)Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo SelectionIEEE Transactions on Multimedia10.1109/TMM.2024.338246926(8758-8770)Online publication date: 2-Apr-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3382469
Zhang YMa BJin LYang YTong C(2024)CosineTR: A dual-branch transformer-based network for semantic line detectionPattern Recognition10.1016/j.patcog.2024.110952(110952)Online publication date: Aug-2024
https://doi.org/10.1016/j.patcog.2024.110952
Lu LWei HJin XZhang YDong BJiang LZhang XLi RZhao YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Aesthetics-Driven Virtual Time-Lapse Photography GenerationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612223(8534-8542)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612223
Ji LPark CRao ZChen QEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Neural Image Popularity Assessment with Retrieval-augmented TransformerProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611918(2427-2436)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611918
He SMing AZheng SZhong HMa HEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)EAT: An Enhancer for Aesthetics-Oriented TransformersProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611881(1023-1032)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611881
Liu XLiu MLi JLiu SWang XLei LZuo W(2023)Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01197(12977-12986)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01197

View Options

Get Access

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents