Creating Computer Vision and Machine Learning Algorithms that Can Analyze Works of Art

When you study a painting, chances are that you can make several inferences about it. In addition to understanding the subject matter, for example, you may be able to classify it by period, style, and artist. Could a computer algorithm "understand" a painting well enough to perform these classification tasks as easily as a human being? We also addressed two other intriguing questions about the capabilities and limitations of AI algorithms: whether they can identify which paintings have had the greatest influence on later artists, and whether they can measure a painting's creativity using only its visual features. We wanted to develop algorithms capable of classifying large groups of paintings by style (for example, as Cubist, Impressionist, Abstract Expressionist, or Baroque), genre (for example, landscape, portrait, or still life), and artist. One requirement for this classification is the ability to recognize color, composition, texture, perspective, subject matter, and other visual features.