Image segmentation using mini-batch K-means

Mini-batch K-means is a variation of the classical K-means technique that we have used so far in this recipe. It is particularly useful to handle large datasets because, rather than using the whole data for each iteration (as K-means does), it uses mini-batches of random data samples to optimize the objective function. In this example, we will do image analysis and create a new image based on the clustered image features.

Getting ready

To perform the example using mini-batch kmeans, make sure you have downloaded the code for this chapter and om.jpg is in the working directory. Then install and load the following packages:

install.packages(c("OpenImageR","ClusterR&quot...