Enhanced K-Means Clustering Algorithm for Image Segmentation

An enhanced K-means clustering algorithm specifically designed for image segmentation applications. This algorithm builds upon the fundamental K-means clustering concept by incorporating advanced color and texture feature extraction capabilities. The implementation typically utilizes color histograms in RGB/HSV color space and texture descriptors like Gabor filters or Local Binary Patterns (LBP) to create comprehensive feature vectors for each pixel or image region. These feature vectors are then processed through an optimized clustering mechanism that employs techniques such as k-means++ initialization for better centroid selection and elbow method for determining optimal cluster count. The algorithm demonstrates superior performance when handling complex images, effectively distinguishing between different objects and background elements through iterative centroid updates and distance calculations using Euclidean or Manhattan distance metrics. This results in significantly improved segmentation accuracy and stability. Furthermore, the algorithm's modular architecture allows easy integration with other image processing techniques through well-defined APIs, enabling combination with edge detection, morphological operations, or deep learning frameworks. The code implementation typically includes parameters for adjusting feature weights, convergence thresholds, and maximum iterations, providing flexibility for various application scenarios. This enhanced K-means clustering algorithm therefore holds substantial practical value in the field of image segmentation, particularly in medical imaging, computer vision, and pattern recognition applications.