Team Splatoon's third installment explores the intricate process of tuning the Colorific algorithm for the Budget Collector dataset. Through the development of "truth" data, hyperparameter optimization, and the creation of an interactive time series visualization, they have made significant strides towards accurately representing the human
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
Team Splatoon introduced the Georgia Tech Spring 2023 Practicum Project where we partner with Budget Collector to determine relationships between colors and their regions, time periods, or other data of interest. We experimented with color quantization methods via k-means and median cut. We found that
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.
The three of us in Team Splatoon have partnered with Budget Collector to analyze relationships between dominant and secondary colors in artwork with region of origin, art period, or time. We will accomplish this by deploying various color quantization techniques that extract key color information from each piece