Abstract
Automatic prediction of the aesthetic quality of a photograph has been an important research problem in image processing and computer vision. While assessing the aesthetic quality of a photograph by human is highly subjective, most existing studies have considered only objective (or general) opinion of multiple viewers. In this paper, we provide a comprehensive investigation of the issue of subjectivity in aesthetic quality assessment using a large-scale database containing photos, user ratings, and user comments. First, we analyze how the mean aesthetic quality level and the level of subjectivity have evolved over time. Second, we examine the feasibility of automatic prediction of the level of subjectivity based on visual features, and identify which features are effective for the prediction. Third, we analyze the users comments given to photos to understand the sources of subjectivity of aesthetic quality rating. Our results show that several factors are simultaneously involved in determining the level of subjectivity of a photo, but it can be predicted with reasonable accuracy. We believe that our results provide insight toward personalized aesthetic photo applications.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Affective Computing |
| DOIs | |
| Publication status | Accepted/In press - 2018 Feb 24 |
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All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
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Objectivity and Subjectivity in Aesthetic Quality Assessment of Digital Photographs. / Kim, Won Hee; Choi, Jun Ho; Lee, Jong Seok.
In: IEEE Transactions on Affective Computing, 24.02.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Objectivity and Subjectivity in Aesthetic Quality Assessment of Digital Photographs
AU - Kim, Won Hee
AU - Choi, Jun Ho
AU - Lee, Jong Seok
PY - 2018/2/24
Y1 - 2018/2/24
N2 - Automatic prediction of the aesthetic quality of a photograph has been an important research problem in image processing and computer vision. While assessing the aesthetic quality of a photograph by human is highly subjective, most existing studies have considered only objective (or general) opinion of multiple viewers. In this paper, we provide a comprehensive investigation of the issue of subjectivity in aesthetic quality assessment using a large-scale database containing photos, user ratings, and user comments. First, we analyze how the mean aesthetic quality level and the level of subjectivity have evolved over time. Second, we examine the feasibility of automatic prediction of the level of subjectivity based on visual features, and identify which features are effective for the prediction. Third, we analyze the users comments given to photos to understand the sources of subjectivity of aesthetic quality rating. Our results show that several factors are simultaneously involved in determining the level of subjectivity of a photo, but it can be predicted with reasonable accuracy. We believe that our results provide insight toward personalized aesthetic photo applications.
AB - Automatic prediction of the aesthetic quality of a photograph has been an important research problem in image processing and computer vision. While assessing the aesthetic quality of a photograph by human is highly subjective, most existing studies have considered only objective (or general) opinion of multiple viewers. In this paper, we provide a comprehensive investigation of the issue of subjectivity in aesthetic quality assessment using a large-scale database containing photos, user ratings, and user comments. First, we analyze how the mean aesthetic quality level and the level of subjectivity have evolved over time. Second, we examine the feasibility of automatic prediction of the level of subjectivity based on visual features, and identify which features are effective for the prediction. Third, we analyze the users comments given to photos to understand the sources of subjectivity of aesthetic quality rating. Our results show that several factors are simultaneously involved in determining the level of subjectivity of a photo, but it can be predicted with reasonable accuracy. We believe that our results provide insight toward personalized aesthetic photo applications.
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UR - http://www.scopus.com/inward/citedby.url?scp=85042706572&partnerID=8YFLogxK
U2 - 10.1109/TAFFC.2018.2809752
DO - 10.1109/TAFFC.2018.2809752
M3 - Article
AN - SCOPUS:85042706572
JO - IEEE Transactions on Affective Computing
JF - IEEE Transactions on Affective Computing
SN - 1949-3045
ER -