“What characterizes personalities of graphic designs?” by Zhao, Cao and Lau

  • ©Nanxuan Zhao, Ying Cao, and Rynson W. H. Lau



Entry Number: 116


    What characterizes personalities of graphic designs?


Session Title: Decision & Style R&E



    Graphic designers often manipulate the overall look and feel of their designs to convey certain personalities (e.g., cute, mysterious and romantic) to impress potential audiences and achieve business goals. However, understanding the factors that determine the personality of a design is challenging, as a graphic design is often a result of thousands of decisions on numerous factors, such as font, color, image, and layout. In this paper, we aim to answer the question of what characterizes the personality of a graphic design. To this end, we propose a deep learning framework for exploring the effects of various design factors on the perceived personalities of graphic designs. Our framework learns a convolutional neural network (called personality scoring network) to estimate the personality scores of graphic designs by ranking the crawled web data. Our personality scoring network automatically learns a visual representation that captures the semantics necessary to predict graphic design personality. With our personality scoring network, we systematically and quantitatively investigate how various design factors (e.g., color, font, and layout) affect design personality across different scales (from pixels, regions to elements). We also demonstrate a number of practical application scenarios of our network, including element-level design suggestion and example-based personality transfer.


    1. Steven Bradley. 2010. How To Use Space In Design, http://vanseodesign.com/web-design/design-space/. (2010).Google Scholar
    2. Zoya Bylinskii, Nam Wook Kim, Peter O’Donovan, Sami Alsheikh, Spandan Madan, Hanspeter Pfister, Fredo Durand, Bryan Russell, and Aaron Hertzmann. 2017. Learning Visual Importance for Graphic Designs and Data Visualizations. In ACM UIST. Google ScholarDigital Library
    3. Ying Cao, Antoni Chan, and Rynson Lau. 2012. Automatic stylistic manga layout. ACM TOG 31, 6 (2012), 141. Google ScholarDigital Library
    4. Ying Cao, Rynson Lau, and Antoni Chan. 2014. Look Over Here: Attention-Directing Composition of Manga Elements. ACM TOG 33, 4 (2014). Google ScholarDigital Library
    5. Huiwen Chang, Fisher Yu, Jue Wang, Douglas Ashley, and Adam Finkelstein. 2016. Automatic Triage for a Photo Series. ACM TOG 35, 4, Article 148 (2016). Google ScholarDigital Library
    6. Siddhartha Chaudhuri, Evangelos Kalogerakis, Stephen Giguere, and Thomas Funkhouser. 2013. Attribit: content creation with semantic attributes. In ACM UIST. 193–202. Google ScholarDigital Library
    7. Carrie Cousins. 2015. How Color, Type and Space Can ImpactMood. https://designshack.net/articles/graphics/how-color-type-and-space-can-impact-mood/. (2015).Google Scholar
    8. Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In Proc. IEEE CVPR. 886–893. Google ScholarDigital Library
    9. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L.i Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In Proc. IEEE CVPR.Google ScholarCross Ref
    10. Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei Efros. 2015. What makes Paris look like Paris? ACM TOG 58, 12 (2015). Google ScholarDigital Library
    11. Elena Garces, Aseem Agarwala, Diego Gutierrez, and Aaron Hertzmann. 2014. A similarity measure for illustration style. ACM TOG 33, 4 (2014). Google ScholarDigital Library
    12. Elena Garces, Aseem Agarwala, Aaron Hertzmann, and Diego Gutierrez. 2017. Style-based exploration of illustration datasets. Multimedia Tools and Applications 76, 11 (2017), 13067–13086. Google ScholarDigital Library
    13. Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, and Sergey Ioffe. 2013. Deep convolutional ranking for multilabel image annotation. arXiv:1312.4894 (2013).Google Scholar
    14. Rebecca Gross. 2015. What It Means to Design With Personality: 25 Awesome Case Studies, https://designschool.canva.com/blog/graphic-art/. (2015).Google Scholar
    15. Michael Gygli, Yale Song, and Liangliang Cao. 2016. Video2gif: Automatic generation of animated gifs from video. In Proc. IEEE CVPR. 1001–1009.Google ScholarCross Ref
    16. Keith Hastings. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 1 (1970), 97–109.Google ScholarCross Ref
    17. Hamid Izadinia, Bryan Russell, Ali Farhadi, Matthew Hoffman, and Aaron Hertzmann. 2015. Deep classifiers from image tags in the wild. In Proc. ACM MM Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions. 13–18. Google ScholarDigital Library
    18. Ali Jahanian, Shaiyan Keshvari, SVN Vishwanathan, and Jan Allebach. 2017. Colors-Messengers of Concepts: Visual Design Mining for Learning Color Semantics. ACM TOCHI 24, 1 (2017), 2. Google ScholarDigital Library
    19. Kara Jensen. 2013. What is the “Look and Feel” of a Website? And Why It’s Important. https://www.bopdesign.com/bop-blog/2013/11/what-is-the-look-and-feel-of-a-website-and-why-its-important/. (2013).Google Scholar
    20. Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, and Holger Winnemoeller. 2013. Recognizing image style. arXiv:1311.3715 (2013).Google Scholar
    21. Janie Kliever. 2015. Designing for Engagement: How Color, Type and Space Can Impact The Mood Of Your Design, https://designschool.canva.com/blog/design-for-engagement/. (2015).Google Scholar
    22. PierreYves Laffont, Zhile Ren, Xiaofeng Tao, Chao Qian, and James Hays. 2014. Transient attributes for high-level understanding and editing of outdoor scenes. ACM TOG 33, 4 (2014). Google ScholarDigital Library
    23. Manfred Lau, Kapil Dev, Weiqi Shi, Julie Dorsey, and Holly Rushmeier. 2016. Tactile Mesh Saliency. ACM TOG 35, 4 (2016). Google ScholarDigital Library
    24. Sharon Lin, Daniel Ritchie, Matthew Fisher, and Pat Hanrahan. 2013. Probabilistic color-by-numbers: Suggesting pattern colorizations using factor graphs. ACM TOG 32, 4 (2013). Google ScholarDigital Library
    25. Zhaoliang Lun, Evangelos Kalogerakis, and Alla Sheffer. 2015. Elements of Style: Learning Perceptual Shape Style Similarity. ACM TOG 34, 4 (2015). Google ScholarDigital Library
    26. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579–2605.Google Scholar
    27. Paul Merrell, Eric Schkufza, Zeyang Li, Maneesh Agrawala, and Vladlen Koltun. 2011. Interactive furniture layout using interior design guidelines. ACM TOG 30, 4 (2011), 87. Google ScholarDigital Library
    28. Nicholas Metropolis, Arianna Rosenbluth, Marshall Rosenbluth, Augusta Teller, and Edward Teller. 1953. Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21, 6 (1953), 1087–1092.Google ScholarCross Ref
    29. Rick Nauert. 2011. Why First Impressions Are Difficult to Change: Study. http://www.livescience.com/10429-impressions-difficult-change-study.html. (2011).Google Scholar
    30. Peter O’Donovan, Aseem Agarwala, and Aaron Hertzmann. 2011. Color compatibility from large datasets. ACM TOG 30, 4 (2011). Google ScholarDigital Library
    31. Peter O’Donovan, Aseem Agarwala, and Aaron Hertzmann. 2014a. Learning layouts for single-page graphic designs. IEEE TVCG 20, 8 (2014), 1200–1213. Google ScholarDigital Library
    32. Peter O’Donovan, Jānis Lībeks, Aseem Agarwala, and Aaron Hertzmann. 2014b. Exploratory font selection using crowdsourced attributes. ACM TOG 33, 4 (2014). Google ScholarDigital Library
    33. Xufang. Pang, Ying Cao, Rynson Lau, and Antoni Chan. 2016. Directing user attention via visual flow on web designs. ACM TOG 35, 6 (2016). Google ScholarDigital Library
    34. Devi Parikh and Kristen Grauman. 2011. Relative attributes. In Proc. IEEE ICCV. 503–510. Google ScholarDigital Library
    35. Christine Phillips and B Chaparro. 2009. Visual appeal vs. usability: which one influences user perceptions of a website more. Usability News (2009), 1–9.Google Scholar
    36. Miriam Redi, Frank Liu, and Neil O’Hare. 2017. Bridging the Aesthetic Gap: The Wild Beauty of Web Imagery. In ACM ICMR. 242–250. Google ScholarDigital Library
    37. Katharina Reinecke, Tom Yeh, Luke Miratrix, Rahmatri Mardiko, Yuechen Zhao, Jenny Liu, and Krzysztof Gajos. 2013. Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In ACM SIGCHI. 2049–2058. Google ScholarDigital Library
    38. Daniel Ritchie, Ankita Kejriwal, and Scott Klemmer. 2011. d. tour: Style-based exploration of design example galleries. In ACM UIST. 165–174. Google ScholarDigital Library
    39. Babak Saleh, Mira Dontcheva, Aaron Hertzmann, and Zhicheng Liu. 2015. Learning style similarity for searching infographics. In Proc. GI. 59–64. Google ScholarDigital Library
    40. Ana Serrano, Diego Gutierrez, Karol Myszkowski, Hans-Peter Seidel, and Belen Masia. 2016. An intuitive control space for material appearance. ACM TOG 35, 6 (2016), 186. Google ScholarDigital Library
    41. Nathan Shedroff and Christopher Noessel. 2012. Make it so: interaction design lessons from science fiction. Rosenfeld Media.Google Scholar
    42. Clayton Silver and William Dunlap. 1987. Averaging correlation coefficients: should Fisher’s z transformation be used? Journal of Applied Psychology 72, 1 (1987), 146.Google ScholarCross Ref
    43. Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014).Google Scholar
    44. Stephan Streuber, M Quiros-Ramirez, Matthew Hill, Carina Hahn, Silvia Zuffi, Alice O’Toole, and Michael Black. 2016. Body talk: Crowdshaping realistic 3D avatars with words. ACM TOG 35, 4 (2016). Google ScholarDigital Library
    45. Aarron Walter. 2012. Redesigning With Personality. https://www.smashingmagazine.com/2012/03/redesigning-with-personality/. (2012).Google Scholar
    46. Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu. 2014. Learning fine-grained image similarity with deep ranking. In Proc. IEEE CVPR. 1386–1393. Google ScholarDigital Library
    47. Mehmet Yumer, Siddhartha Chaudhuri, Jessica Hodgins, and LeventBurak Kara. 2015. Semantic shape editing using deformation handles. ACM TOG 34, 4 (2015). Google ScholarDigital Library
    48. Matthew Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv:1212.5701 (2012).Google Scholar
    49. Matthew Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In Proc. IEEE ECCV. 818–833.Google ScholarCross Ref
    50. Fang Zhao, Yongzhen Huang, Liang Wang, and Tieniu Tan. 2015. Deep semantic ranking based hashing for multi-label image retrieval. In Proc. IEEE CVPR. 1556–1564.Google Scholar

ACM Digital Library Publication: