Implementation of K-Means Clustering Algorithm for Color Image Segmentation

K-means clustering algorithm is a classic unsupervised learning method commonly used to partition data into K distinct clusters. In color image segmentation tasks, the K-means algorithm can automatically group pixels based on color features, thereby achieving effective image segmentation.

In the MATLAB environment, implementing color image segmentation involves several key steps. First, the target color image needs to be read, and the RGB values of its pixels serve as feature inputs. Since each pixel in a color image contains three-channel values (red, green, blue), each pixel can be treated as a point in three-dimensional space. The K-means algorithm's function is to partition these points into K distinct clusters.

The K-means algorithm operates through iterative optimization to update cluster centers and pixel classifications. Initially, K center points are randomly selected. Then, the distance from each pixel to these centers is calculated, assigning pixels to the nearest cluster. Subsequently, the center of each cluster is recalculated, and the process repeats until centers stabilize or maximum iterations are reached.

In implementation, MATLAB provides various utility functions to simplify coding. For example, built-in functions can preprocess images and convert RGB images into appropriate array formats for K-means computation. MATLAB's kmeans function efficiently performs clustering tasks, after which clustering results are mapped back to the image to obtain segmented results. Key implementation commands include imread() for image loading, reshape() for data dimension transformation, and kmeans() for core clustering operations.

By adjusting the K value (number of desired segmentation regions), the granularity of image segmentation can be controlled. Larger K values produce more detailed segmentation results but may introduce over-segmentation issues, requiring appropriate parameter selection based on application scenarios. The final segmentation results clearly display dominant color regions in images, making them suitable for various computer vision tasks such as object detection and background separation.