MATLAB Implementation of ISODATA Algorithm with Optimized Performance

This article explores the ISODATA algorithm implementation for image segmentation tasks. As a significant technique in computer vision, ISODATA enables enhanced understanding of image information through dynamic cluster analysis. Our implementation simplifies the original algorithm while maintaining core functionality through strategic MATLAB optimizations. Key enhancements include vectorized distance calculations using pdist2() for efficient centroid-to-sample comparisons, automated threshold-based cluster merging with intersect() functions, and intelligent splitting criteria using eigenvalue decomposition via eig() for covariance matrices. These modifications significantly accelerate processing speed without compromising segmentation accuracy, making the algorithm particularly suitable for handling large-scale image datasets. The streamlined approach maintains the algorithm's adaptive clustering capabilities while reducing computational overhead through optimized matrix operations and logical indexing. We demonstrate that this balanced implementation preserves essential ISODATA features including dynamic cluster number adjustment and statistical pattern recognition, providing practical efficiency gains for real-world computer vision applications.