R: k-Means Clustering on Imaging
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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.
We will utilize the following packages for input and output:
The image is represented by large array of pixels with dimension rows by columns by channels — red, green, and blue or RGB.
Plot the clustered colours:
Possible clusters of pixels on different k-Means:
I suggest you try it!
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 |
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:
| Original | k = 6 | |
|---|---|---|
Table 1: Different k-Means Clustering. | ||
 |  | |
| k = 5 | k = 4 | |
 |  | |
| k = 3 | k = 2 | |
 |  | |
I suggest you try it!
Reference
- K-means clustering. Wikipedia. Retrieved September 11, 2014.
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