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
Find out about Art Scene's latest app update, which balances the concept of 'slow tech' with an enhanced user experience. The update improves the app's backend infrastructure, resulting in a faster art rating process. Additionally, the app's acceptance into the Apple Store marks a significant
Discover how artists can turn Instagram likes into dollars by optimizing their profile, posting high-quality images, engaging with their audience, and using hashtags effectively. Learn strategies to boost your art business on Instagram.
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
Welcome to Art Scene: Meet More Friends, formerly known as AI Art Advisor. Our app has undergone a rebranding journey to better cater to a wider audience, particularly those in the middle class, who are interested in discovering and understanding the world of art. While
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