Classical Iterative Histogram-Based Clustering Algorithm for Image Segmentation

This document introduces a classical iterative histogram separation clustering algorithm designed for image analysis applications. The algorithm utilizes image histogram distributions to achieve effective multi-class object segmentation. A key implementation approach involves iteratively separating histogram peaks corresponding to different object classes through threshold optimization. Typical code implementation would include functions for histogram calculation, peak detection using derivative analysis, and iterative threshold adjustment based on valley points between peaks. The algorithm's notable advantage lies in its exceptional computational speed, making it highly efficient for processing large datasets. By separating the image histogram into distinct clusters, the algorithm accurately segments different target categories with minimal computational overhead. This combination of efficiency and accuracy establishes it as an essential tool in image processing workflows. Through this documentation, you will gain comprehensive understanding of the algorithm's underlying principles and practical implementation strategies.