Adversarial Learning Approach to Unsupervised Labeling of Fine Art Paintings
Catherine Sandoval, Elena Pirogova, Margaret Lech
Abstract
An automatic classification of fine art images is limited by the scarcity of high-quality labels made by art experts. This study aims to provide meaningful automatic labeling of fine art paintings (machine labeling) without the need for human annotation. A new unsupervised Adversarial Clustering System (ACS) is proposed. The ACS is an adversarial learning approach comprising an unsupervised clustering module generating machine labels and a supervised classification module classifying the data based on the machine labels. Both modules are linked through an optimization algorithm iteratively improving the unsupervised clusters. The objective function driving the improvement consists of the within-cluster sum of squares (WCSS) error and the supervised classification accuracy. The proposed method was tested on three different fine-art datasets, including two sets of paintings previously categorized by art experts and one never categorized collection of Australian Aboriginal paintings. The unsupervised clusters were analyzed using standard unsupervised clustering metrics and a reliability measure between machine and human labeling. The ACS showed higher reliability compared to the classical k-means clustering method. The content analysis of unsupervised clusters indicated grouping based on scene composition, type, and shape of the object, edge sharpness and direction, and color palette.