And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model

Autoři: Baixi Xing aff001;  Kejun Zhang aff002;  Lekai Zhang aff001;  Xinda Wu aff002;  Huahao Si aff003;  Hui Zhang aff002;  Kaili Zhu aff002;  Shouqian Sun aff002
Působiště autorů: Institute of Industrial Design, Zhejiang University of Technology, Hangzhou, China aff001;  College of Computer Science and Technology, Zhejiang University, Hangzhou, China aff002;  School of Media and Design, Hangzhou Dianzi University, Hangzhou, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers’ ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.

Klíčová slova:

Algorithms – Artificial intelligence – Gene pool – Imaging techniques – Machine learning – Machine learning algorithms – Neural networks – Support vector machines


1. Brunner R, Emery S, Hall R. Do You Matter? How Great Design Will Make People Love Your Company. FT Press, U.S., 2008.

2. Norman D A. Emotional Design: Why We Love (Or Hate) Everyday Things. Basic Books, U.S., 2004.

3. Law D, Cheung M, Yip J, Yick K, Wong C. Scoliosis brace design: influence of visual aesthetics on user acceptance and compliance. Ergonomics. 2017; 876–886. doi: 10.1080/00140139.2016.1227093 27547883

4. Hou G, Lu G. The influence of design proposal viewing strategy: design aesthetics and professional background. Int J Technol Des Educ. 2019; 29:543–564.

5. Guo F, Li M, Hu M, Li F, Lin B. Distinguishing and quantifying the visual aesthetics of a product: An integrated approach of eye-tracking and EEG. International Journal of Industrial Ergonomics. 2019; 47–56.

6. Chien C, Kerh R, Lin K, Yu A P. Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design. Computers & Industrial Engineering. 2016; 162–173.

7. Bloch P H, Brunel F F, Arnold T J. Individual differences in the centrality of visual product aesthetics: concept and measurement. J. Consum. Res. 2003; 551–565.

8. Hsiao K L, Chen C C. What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telematics Inf. 2018; 103–113.

9. Toufani S, Stanton J P, Chikweche T. The importance of aesthetics on customers' intentions to purchase smartphones. Market. Intell. Plann. 2017; 316–338.

10. Simmonds G, Spence C. Thinking inside the box: how seeing products on, or through, the packaging influences consumer perceptions and purchase behaviour. Food Qual. Prefer. 2017; 340–351.

11. Nanda P, Bos J, Kramer K, Hay C, Ignacz J. Effect of smartphone aesthetic design on users' emotional reaction: an empirical study. The TQM Journal. 2008; 348–355.

12. Schindler I, Hosoya G, Menninghaus W, Ursula B, Valentin W, Michael E, et al. Measuring aesthetic emotions: A review of the literature and a new assessment tool. Plos One. 2017; 12(6): e0178899. doi: 10.1371/journal.pone.0178899 28582467

13. Tian X, Long Y, Lv H. Relative Aesthetic Quality Ranking. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 2018; 2509–2516.

14. Liao W, Chen P. Analysis of Visual Elements in Logo Design. Proceedings of International Symposium on Smart Graphics. 2014; 73–85.

15. Sheng K, Dong W, Ma C, Mei X, Huang F, Hu B. Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment. Proceedings of ACM Mulimedia. 2018; 879–886.

16. Qian X, Li C, Lan K, Hou X, Li Z, Han J. POI Summarization by Aesthetics Evaluation from Crowd Source Social Media. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2018; 27(3): 1178–1189. doi: 10.1109/TIP.2017.2769454 29220319

17. Ren J, Shen X, Lin Z, Mech R, Foran D J. Personalized Image Aesthetics. Proceedings of IEEE International Conference on Computer Vision. 2017; 638–647.

18. Kucer M, Loui A C, Messinger D W. Leveraging Expert Feature Knowledge for Predicting Image Aesthetics. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2018; 27(10): 5100–5113.

19. Chen R, Hua L, Xie Y, Lin T, Tang N. A Fuzzy-Rule-Based Approach for Webpage Aesthetics Modeling. Proceedings of Nicograph International. 2016; 142–143.

20. Maity R, Bhattacharya S. Is My Interface Beautiful?—A Computational Model-Based Approach. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS. 2019; 6(1): 149–162.

21. Persada A G, Pranata M W A, Ana A A. Aesthetics of Interaction Design on the Mobile-Based University Website. Proceedings of International Conference on Electrical Engineering and Computer Science. 2017; 137–143.

22. Wu T, Zhang L, Yang J. Automatic Generation of Aesthetic Patterns with Cloud Model. Proceedings of 12th International Conference on Natural Computation. Fuzzy Systems and Knowledge Discovery. 2016; 1077–1084.

23. Zhang C, Lei K, Jia J. AI Painting: An Aesthetic Painting Generation System. Proceedings of ACM Multimedia. 2018; 1231–1234.

24. Erdem A N, Halici U. Applying Computational Aesthetics to a Video Game Application Using Machine Learning. IEEE Computer Graphics and Applications. 2016; 36(4): 23–33. doi: 10.1109/MCG.2016.43 27244720

25. Ross B J, Ralph W, Zong H. Evolutionary Image Synthesis Using a Model of Aesthetics. Proceedings of IEEE Congress on Evolutionary Computation. 2006; 1087–1093.

26. Wong L, Low K. SALIENCY-ENHANCED IMAGE AESTHETICS CLASS PREDICTION. Proceedings of the International Conference on Image Processing. 2009; 997–1001.

27. Su H, Chen T, Kao C, Hsu W H, Chien S. Preference-Aware View Recommendation System for Scenic Photos Based on Bag-of-Aesthetics-Preserving Features. IEEE TRANSACTIONS ON MULTIMEDIA. 2012; 14(3): 833–844.

28. Lovato P, Bicego M, Segalin C, Perina A, Sebe N, Cristani M. Faved! Biometrics: Tell Me Which Image You Like and I’ll Tell You Who You Are. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. 2014; 9(3): 364–375.

29. Zhang L, Gao Y, Zimmermann R, Tian Q, Li X. Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2014; 23(3): 1419–1430. doi: 10.1109/TIP.2014.2303650 24723537

30. Tarvainen J, Sjöberg M, Westman S, Laaksonen J, Oittinen P. Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments. IEEE TRANSACTIONS ON MULTIMEDIA. 2014; 16(8): 2085–2098.

31. Temel D, AlRegib G. A COMPARATIVE STUDY OF COMPUTATIONAL AESTHETICS. Proceedings of the International Conference on Image Processing. 2014; 590–595.

32. Wu O, Zuo H, Hu W, Li B. Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning. IEEE TRANSACTIONS ON MULTIMEDIA. 2016; 18(6): 1062–1077.

33. Lu X, Lin Z, Jin H, Yang J, Wang J Z. Rating Image Aesthetics Using Deep Learning. IEEE TRANSACTIONS ON MULTIMEDIA. 2015; 17(11): 2021–2035.

34. Jin B, Segovia M V O, Susstrunk S. IMAGE AESTHETIC PREDICTORS BASED ON WEIGHTED CNNS. Proceedings of the International Conference on Image Processing. 2016; 2291–2296.

35. Lee H, Hong K, Kang H, Lee S. Photo Aesthetics Analysis via DCNN Feature Encoding. IEEE TRANSACTIONS ON MULTIMEDIA. 2017; 19(8): 1921–1933.

36. Liu Z, Wang Z, Yao Y, Zhang L, Shao L. Deep Active Learning with Contaminated Tags for Image Aesthetics Assessment. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2019; doi: 10.1109/TIP.2018.2828326 29993633

37. Wang W, Shen J. Deep Cropping via Attention Box Prediction and Aesthetics Assessment. Proceedings of IEEE International Conference on Computer Vision. 2017; 2205–2214.

38. Tong S, Liang X, Iwaki S, Tosa N. Learning the Cultural Consistent Facial Aesthetics by Convolutional Neural Network. Proceedings of International Conference on Culture and Computing. 2017; 97–104.

39. Fu X, Yan J, Fan C. IMAGE AESTHETICS ASSESSMENT USING COMPOSITE FEATURES FROM OFF-THE-SHELF DEEP MODELS. Proceedings of the International Conference on Image Processing. 2018; 3528–3533.

40. Iqbal A, Heijden H V D, Guid M, Makhmali A. Evaluating the Aesthetics of Endgame Studies: A Computational Model of Human Aesthetic Perception. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES. 2012; 4(3): 178–192.

41. Browne C. Elegance in Game Design. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES. 2012; 4(3): 229–241.

42. Murray N, Marchesotti L, Perronnin F. AVA: A Large-Scale Database for Aesthetic Visual Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2012; 2408–2416.

43. Meng X, Gao F, Shi S, Zhu S, Zhu J. MLANs: Image Aesthetic Assessment via Multi-Layer Aggregation Networks. Proceedings of International Conference on Image Processing Theory, Tools and Applications. 2018; doi: 10.1109/IPTA.2018.8608132

44. Sidhu D M, Mcdougall K H, Jalava S T, Glen E B. Prediction of beauty and liking ratings for abstract and representational paintings using subjective and objective measures. PLOS ONE. 2018; 13(7): e0200431. doi: 10.1371/journal.pone.0200431 29979779

45. Fan R E, Chang K W, Hsieh C J, Wang X R, Lin C J. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research. 2008; 9: 1871–1874.

46. Chen W, Yan X, Zhao Z, Hong H, Bui D T, Pradhan B. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bulletin of Engineering Geology and the Environment. 2018; 1–20.

47. Ho T K. The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998; 20(8): 832–844.

48. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proceedings of International Conference on Learning Representations. San Diego, USA. 2015.

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2020 Číslo 1
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