Paper Info
Title
Aesthetic Visual Quality Assessment of Paintings
Abstract
This paper aims to evaluate the aesthetic visual quality of a special type of visual media: digital images of paintings. Assessing the aesthetic visual quality of paintings can be considered a highly subjective task. However, to some extent, certain paintings are believed, by consensus, to have higher aesthetic quality than others. In this paper, we treat this challenge as a machine learning problem, in order to evaluate the aesthetic quality of paintings based on their visual content. We design a group of methods to extract features to represent both the global characteristics and local characteristics of a painting. Inspiration for these features comes from our prior knowledge in art and a questionnaire survey we conducted to study factors that affect human's judgments. We collect painting images and ask human subjects to score them. These paintings are then used for both training and testing in our experiments. Experimental results show that the proposed work can classify high-quality and low-quality paintings with performance comparable to humans. This work provides a machine learning scheme for the research of exploring the relationship between aesthetic perceptions of human and the computational visual features extracted from paintings.
Year
DOI
Venue
2009
10.1109/JSTSP.2009.2015077
J. Sel. Topics Signal Processing
Keywords
Field
DocType
art,painting,computational visual features,image processing,learning (artificial intelligence),digital images,index terms— visual quality assessment,painting images,visual quality assessment,aesthetics,feature extraction,machine learning problem,visual perception,classification,paintings,aesthetic visual quality assessment,machine learning,digital image,visualization,learning artificial intelligence,questionnaire survey,computer vision,testing
Computer vision,Visualization,Computer science,Painting,Feature extraction,Artificial intelligence,Visual media,Questionnaire,Perception,Visual perception,Visual rhetoric
Journal
Volume
Issue
ISSN
3
2
1932-4553
Citations 
PageRank 
References 
91
5.10
17
Authors
2
Name
Order
Citations
PageRank
Congcong Li124016.48
Tsuhan Chen24763346.32