K-means Segmentation | 555Tools — Online Tools for FPGA & Algorithm Engineers

Image AlgorithmsTool Introduction

K-means treats each pixel's gray value as a feature vector, iteratively converging through E-step (nearest centroid assignment) and M-step (intra-class mean update) to achieve unsupervised clustering segmentation. Adjusting k controls segmentation granularity — a teaching bridge for understanding unsupervised machine learning in image processing.

Key Formula / Principle

E-step: label(i) = argmin_c ||gray(i) − centroid_c||²
M-step: centroid_c = mean(gray(i) for label(i)==c)
Converge: max|Δcentroid| < ε or assignments unchanged

Data Processed Locally|All computations run in your browser. Images, formulas, state machine definitions, and any other input data are never sent to any server.

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