Subject Bias in Image Aesthetic Appeal Ratings

Data Science: Journal of Computing and Applied Informatics

View Publication Info
 
 
Field Value
 
Title Subject Bias in Image Aesthetic Appeal Ratings
 
Creator Siahaan, Ernestasia
Nababan, Esther
 
Description Automatic prediction of image aesthetic appeal is an important part of multimedia and computer vision research, as it contributes to providing better content quality to users. Various features and learning methods have been proposed in the past to predict image aesthetic appeal more accurately. The effectiveness of these proposed methods often depend on the data used to train the predictor. Since aesthetic appeal is a subjective construct, factors that influence the subjectivity in aesthetic appeal data need to be understood and addressed. In this paper, we look into the subjectivity of aesthetic appeal data, and how it relates with image characteristics that are often used in aesthetic appeal prediction. We use subject bias and confidence interval to measure subjectivity, and check how they might be influenced by image content category and features.
 
Publisher Talenta Publisher
 
Date 2017-07-18
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://talenta.usu.ac.id/index.php/JoCAI/article/view/63
10.32734/jocai.v1.i1-63
 
Source Data Science: Journal of Computing and Applied Informatics; Vol 1 No 1 (2017): Data Science: Journal of Computing and Applied Informatics (JoCAI); 13-20
2580-829X
2580-6769
 
Language eng
 
Relation https://talenta.usu.ac.id/index.php/JoCAI/article/view/63/28
 
Rights Copyright (c) 2017 Journal of Computing and Applied Informatics
 

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