Artist Identification for Renaissance Paintings

Current work in author identification is primarily directed towards music classification. Identification of artists by analyzing features of their work has recently gained interest. As a multiclass classification problem, potentially applicable machine learning approaches to the problem are numerous. We propose to extend present work in this area, which uses Naïve Bayes classifiers and multi-class SVMs, by picking a more unique set of paintings across prolific artists. We initially use a histogram of colors as our features, and then we analyze more advanced features like the histogram of gradient orientations (HOG). Due to the large number of features as compared to the number of paintings, we use PCA to condition features based on the highest variance. We apply several multi-class classification techniques like Naïve Bayes, Linear Discriminant Analysis, Logistic Regression, K-Means and SVMs to our problem and achieve a maximum classification accuracy of 65% for an unknown painting across 5 artists.