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
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
Previous Blog TLDR: Our goal of this post is to implement color quantization in R, compare several methods of detecting segments of an image, and start creating the visualization UI. It’s no surprise that this topic naturally lends itself to some preliminary data exploration and visualization, and
This team from Georgia Tech discuss the process of data cleaning and analysis in the context of studying color patterns in artworks. They used a dataset from the Art Institute of Chicago, consisting of 122,435 artworks, for this purpose. The data was cleaned to extract
This blog post titled "Giving Color Vision to a Machine" by Global Data Artists delves into the intricate process of training a machine to recognize and categorize similar colors within different images. The authors detail their approach of employing KMeans clustering on image data, confronting
This article explores extracting the top 5 dominant colors from images using K-Means Clustering Algorithm. They utilize a dataset provided by Budget Collector, which they access through Airtable API in Python. The KMeans clustering algorithm from the sklearn library is used to find clusters of
Explore the fascinating connections between color changes and global art movements in the "Making Art Sm‘art’er" article. Delve into the challenges and innovative solutions used in grouping paintings based on color trends, frontend frameworks like D3 + Svelte, and the utilization of pixel clustering
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