지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.5
- 수록면
- 39 - 55 (17page)
- DOI
- 10.15187/adr.2020.05.33.2.39
이용수
초록· 키워드
오류제보하기
Background : Advances in data science have allowed us to investigate human emotions based on the learning of the data set, with current technology enabling us to research the relationship between the form elements of graphic images and human emotional responses. However, to date, studies have been limited to photography, meaning that the practice of graphic design remains unexplored.
Methods : We used a design workshop to exploit graphic elements that are perceptually relevant. By identifying and quantifying the elements of each image (N = 320), we were able to define the machine learning features. We collected emotion assessments relating to the images with regard to pleasure, arousal, and dominance using a Likert scale, and combined them with the features to build a dataset. The assessments were carried out twice to form a baseline of accuracy. Applying machine learning methods such as kknn, svmRadial, and C5.0, we modeled algorithms that predict individuals’ emotional assessments of specific aspects.
Results : Human assessments were repeatedly in the range of 62.81–80.93%, and a prediction accuracy between 52.26–80.32% was achieved. In particular, the prediction accuracy of pleasure and dominance aspects was relatively high across all individuals.
Conclusions : This study demonstrates the use of machine learning-driven algorithm modeling to predict assessments of emotion based on graphical elements of movie posters. Overall, predictions of pleasure and dominance aspects showed higher accuracy than those of the arousal aspect. Limitations and further investigations are discussed to obtain not only a more accurate but also a more insightful estimation.
Methods : We used a design workshop to exploit graphic elements that are perceptually relevant. By identifying and quantifying the elements of each image (N = 320), we were able to define the machine learning features. We collected emotion assessments relating to the images with regard to pleasure, arousal, and dominance using a Likert scale, and combined them with the features to build a dataset. The assessments were carried out twice to form a baseline of accuracy. Applying machine learning methods such as kknn, svmRadial, and C5.0, we modeled algorithms that predict individuals’ emotional assessments of specific aspects.
Results : Human assessments were repeatedly in the range of 62.81–80.93%, and a prediction accuracy between 52.26–80.32% was achieved. In particular, the prediction accuracy of pleasure and dominance aspects was relatively high across all individuals.
Conclusions : This study demonstrates the use of machine learning-driven algorithm modeling to predict assessments of emotion based on graphical elements of movie posters. Overall, predictions of pleasure and dominance aspects showed higher accuracy than those of the arousal aspect. Limitations and further investigations are discussed to obtain not only a more accurate but also a more insightful estimation.
목차
Abstract
1. Introduction
2. Design Workshop
3. Feature Extraction of Movie Posters toward the Machine-Learning Algorithm Modelling
4. Algorithm modeling using machine-learning methods
5. Discussion and conclusion
References
참고문헌 (35)
참고문헌 신청
Gastón Ares / 2016 / Influence of label design on children’s perception of two snack foods: Comparison of rating and choice-based conjoint analysis / Food Quality and Preference 53:1~8
Baek, Y. / 2019 / Character region awareness for text detection / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition :9365~9374
Beyer, H. / 1999 / Contextual design / Interactions 6(1):32~42
John Canny / 1986 / A Computational Approach to Edge Detection / IEEE Transactions on Pattern Analysis and Machine Intelligence (6):679~698
Cho, J. / 2017 / Palettenet: Image recolorization with given color palette / Proceedings of the ieee conference on computer vision and pattern recognition workshops :62~70
Baek, Y. / 2019 / Character region awareness for text detection / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition :9365~9374
Beyer, H. / 1999 / Contextual design / Interactions 6(1):32~42
John Canny / 1986 / A Computational Approach to Edge Detection / IEEE Transactions on Pattern Analysis and Machine Intelligence (6):679~698
Cho, J. / 2017 / Palettenet: Image recolorization with given color palette / Proceedings of the ieee conference on computer vision and pattern recognition workshops :62~70
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