The "Image Segmentation by Clustering" project aims to segment images into meaningful regions using clustering techniques. The primary method employed is K-means clustering, which groups pixels based on their color values. This project explores the effectiveness of K-means clustering in segmenting images and finding the optimal number of clusters using the Elbow method.
Image segmentation is a crucial task in computer vision and image processing, where an image is partitioned into multiple segments or regions to simplify its analysis. Clustering is a technique used to group similar data points, and in this project, it is applied to segment images based on pixel characteristics. The goal is to divide an image into segments that represent different objects or regions within the image.
Data Collection: The project uses an image as input, which is read using OpenCV and converted to RGB format.
Data Preprocessing: The image is reshaped into a 2D array of pixels, and the pixel values are converted to float32 for processing.
K-means Clustering: The K-means clustering algorithm is applied to group pixels into clusters. The number of clusters (K) is initially set to a predefined value.
Finding Optimal K-value: The Elbow method is used to determine the optimal number of clusters by plotting the sum of squared distances (SSD) for different values of K and identifying the point of maximum curvature.
Segmentation: The labels from the K-means clustering are mapped to the center values to create the segmented image.
Visualization: The original and segmented images are displayed using Matplotlib.
The project conducts experiments with different values of K to observe the segmentation results. The Elbow method is used to find the optimal number of clusters. The experiments involve:
Reading and preprocessing the image.
Applying K-means clustering with different values of K.
Plotting the SSD for each value of K to identify the optimal number of clusters.
Visualizing the segmented images for different values of K.
The results of the experiments show the segmented images for different values of K. The Elbow method helps identify the optimal number of clusters, which provides the best segmentation results. The segmented images demonstrate how K-means clustering can effectively partition an image into meaningful regions based on pixel characteristics
The "Image Segmentation by Clustering" project successfully demonstrates the application of K-means clustering for image segmentation. The methodology and experiments highlight the importance of selecting the optimal number of clusters for effective segmentation. The project provides a foundation for further exploration of clustering techniques in image processing and computer vision
