R: k-Means Clustering on an Image

September 12, 2014
By

(This article was first published on Analysis with Programming, and kindly contributed to R-bloggers)

Enough with the theory we recently published, let’s take a break and have fun on the application of Statistics used in Data Mining and Machine Learning, the k-Means Clustering.
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. (Wikipedia, Ref 1.)

We will apply this method to an image, wherein we group the pixels into k different clusters. Below is the image that we are going to use,

Colorful Bird From Wall321

We will utilize the following packages for input and output:

  1. jpeg – Read and write JPEG images; and,
  2. ggplot2 – An implementation of the Grammar of Graphics.


Download and Read the Image

Let’s get started by downloading the image to our workspace, and tell R that our data is a JPEG file.


Cleaning the Data

Extract the necessary information from the image and organize this for our computation:

The image is represented by large array of pixels with dimension rows by columns by channels — red, green, and blue or RGB.

Plotting

Plot the original image using the following codes:

Clustering

Apply k-Means clustering on the image:

Plot the clustered colours:

Possible clusters of pixels on different k-Means:

Original k = 6
Table 1: Different k-Means Clustering.
k = 5 k = 4
k = 3 k = 2

I suggest you try it!

Reference

  1. K-means clustering. Wikipedia. Retrieved September 11, 2014.

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