Explore the fascinating world of color extraction and computer vision in our latest article. Discover how k-means clustering, an unsupervised machine learning algorithm, is used to extract dominant colors from paintings. Learn about raster and vector graphics, RGB color spaces, and the comparison between K-means
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.