Georgia Tech students explore color recognition in artworks through an innovative fusion of APIs and machine learning. The research utilizes the Art Institute of Chicago's API for metadata acquisition, combined with a color clustering algorithm, allowing for a detailed statistical analysis of color usage in
This article explores the use of clustering algorithms and art museum APIs to extract dominant color palettes from artworks, with a focus on the collection of the Art Institute of Chicago. The authors demonstrate how to programmatically access image and metadata, extract color statistics, and
The article discusses a project to analyze patterns in the use of colors in paintings by using a 3D K-Means clustering algorithm to extract the dominant colors in images and their associated metadata, and then using this data to perform time series and spatial regression.