// Code Glimpse

Understanding Image Segmentation With K-Means Clustering

Overview

Image Segmentation has become a crucial aspect of Machine Learning and AI. It is the process of partitioning an image into multiple segments or sets of pixels, also known as superpixels. Each segment captures the area in the image which we will further use for analysis.

One common technique to perform image segmentation is using K-Means Clustering - a popular unsupervised machine learning algorithm.

In this article, we are going to delve deep into the application of K-Means Clustering for Image Segmentation and see how it works under the hood.

Let's get started!

Requirements

This tutorial assumes that you have basic knowledge of Python and a fundamental understanding of Machine Learning and Image Processing. Here are the Python packages necessary for our task:

import cv2
import numpy as np
from sklearn.cluster import KMeans

K-Means Clustering for Image Segmentation

Presented below is a step-by-step tutorial on how to segment an image using K-Means Clustering.

  1. Load an Image:

We use the cv2 library for reading the image. It reads the image in BGR format.

image = cv2.imread('path_to_your_image.jpeg')
cv2.imshow('Original Image', image)
cv2.waitKey(0)
  1. Preprocess Image:

The KMeans algorithm works on a 2-dimensional array and the image is a 3-dimensional array (height, width, channels), hence we need to convert our image into a two-dimensional array.

image = image.reshape(-1, 3)
  1. Apply KMeans to the Image:

Now, we instantiate the KMeans algorithm and apply it to our image.

kmeans = KMeans(n_clusters=3)	# 3 clusters indicating 3 segments in image
image_kmeans = kmeans.fit_predict(image)
  1. Visualize the Segmented Image:

Finally, we will convert our flatten segmented image to its original shape and visualize it.

image_kmeans = image_kmeans.reshape(-1, image.shape[1])
cv2.imshow('Segmented Image', image_kmeans)
cv2.waitKey(0)

And, here you go! You successfully performed the image segmentation using K-Means Clustering.

Conclusion

This is just a glimpse of the power of machine learning in image processing. Image segmentation has wide applications in various domains, like medical imaging, autonomous vehicles, etc. And with the right application of ML techniques, there is no end to what you can achieve.

Happy learning!