shrayanroy / color_kmeans

Different Applications of K-Means Clustering in Image Analysis

K-Means Pixels: Image Color Grouping and Analysis

This Repository explores the applications of K-Means Clustering in Image Analysis. K-Means Clustering is a simple method, which tries to find homogeneous subgroups among observations. We will use these ideas to find the Color Palette of the Image, Color Harmonization, Color Compression, and many more.

Languages Used: R, C++

Some Examples :

A. Finding Color Palette :

Here with K = 3, the India Gate is properly characterized.

B. Color Harmonization using Closest-Color Approach.