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In the past few years, the number of fine-art collections that are digitizedand publicly available has been growing rapidly. With the availability of suchlarge collections of digitized artworks comes the need to develop multimediasystems to archive and retrieve this pool of data. Measuring the visualsimilarity between artistic items is an essential step for such multimediasystems, which can benefit more high-level multimedia tasks. In order to modelthis similarity between paintings, we should extract the appropriate visualfeatures for paintings and find out the best approach to learn the similaritymetric based on these features. We investigate a comprehensive list of visualfeatures and metric learning approaches to learn an optimized similaritymeasure between paintings. We develop a machine that is able to makeaesthetic-related semantic-level judgments, such as predicting a painting'sstyle, genre, and artist, as well as providing similarity measures optimizedbased on the knowledge available in the domain of art historicalinterpretation. Our experiments show the value of using this similarity measurefor the aforementioned prediction tasks.

Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature