An Unsupervised Learning Approach Using NHANES Data

Exploring Art Movements through Computational Color Analysis

This paper applies K-Means clustering for patch-based image inpainting: it learns a texture dictionary from intact grayscale regions, then uses it to predict missing pixels. The method is computationally efficient, validated on sparse random damage, and performance is measured as corruption increases.