Have you ever wondered how we can implement a machine learning algorithm on the pixel intensity value with a common K-means clustering algorithm? In this method, we would generate a compressed variant of our picture with more scattered colours. The image will be processed in a lower intensity resolution, whereas the fraction of pixels will prevail. This procedure is very interesting, so I expect that you will like it. This article can appear as a particularly impressive and unexpected one, so here is the link to the article, please have a read and hope you like it.

This post is a simple yet illustrative application of K-means clustering technique. Using K-means clustering, we will perform quantization of colours present in the image which will further help in compressing the image. In a coloured image, each pixel is of size 3 bytes (RGB), where each colour can have intensity values from 0 to 255. Following combinatorics, the total number of colours which can be represented are 256*256*256. Practically, we are able to visualize only a few colours in an image.

This post is a simple yet illustrative application of K-means clustering technique. Using K-means clustering, we will perform quantization of colours present in the image which will further help in compressing the image. In a coloured image, each pixel is of size 3 bytes (RGB), where each colour can have intensity values from 0 to 255. Following combinatorics, the total number of colours which can be represented are 256*256*256. Practically, we are able to visualize only a few colours in an image.