MATLAB Implementation of C-Means Clustering for Scene Segmentation

In this scene segmentation task, we can implement the C-means clustering algorithm using MATLAB. C-means clustering, also known as K-means clustering, is a widely used unsupervised learning algorithm that partitions data points into distinct clusters based on feature similarity. For image processing applications, we treat each pixel in the image as a data point characterized by its color values (RGB or grayscale) and spatial coordinates. The MATLAB implementation typically involves using the kmeans() function, which requires specifying the number of clusters (K) and can utilize different distance metrics like Euclidean or Manhattan distance. The algorithm iteratively assigns pixels to the nearest cluster centroid and updates centroid positions until convergence. Through C-means clustering, we can effectively identify different objects or regions within the image and label them accordingly, enabling subsequent image processing tasks such as object recognition, boundary detection, and region analysis. The MATLAB implementation often includes preprocessing steps like color space conversion and feature normalization, followed by post-processing morphological operations to refine the segmentation results. Therefore, utilizing MATLAB's C-means clustering algorithm for scene segmentation represents an efficient and commonly employed approach in computer vision applications.