Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective Dataset of Paintings
This work addresses computational aesthetics for art analysis, but it is incremental as it focuses on validating color features in a new dataset.
The paper tackled the problem of assessing aesthetic quality in paintings by analyzing color features, achieving a classification rate of up to 73% in correlating color features with subjective scores using a novel dataset of Western paintings.
Computational aesthetics is an emerging field of research which has attracted different research groups in the last few years. In this field, one of the main approaches to evaluate the aesthetic quality of paintings and photographs is a feature-based approach. Among the different features proposed to reach this goal, color plays an import role. In this paper, we introduce a novel dataset that consists of paintings of Western provenance from 36 well-known painters from the 15th to the 20th century. As a first step and to assess this dataset, using a classifier, we investigate the correlation between the subjective scores and two widely used features that are related to color perception and in different aesthetic quality assessment approaches. Results show a classification rate of up to 73% between the color features and the subjective scores.