Measuring Image Similarity Based on Color Histograms with MATLAB Implementation

This article introduces an image similarity measurement method based on color histograms. The approach evaluates similarity by comparing the color distribution patterns of images. Specifically, we employ a histogram-based algorithm to calculate similarity between images. The algorithm first converts images into color histogram representations using MATLAB's histogram computation functions, then calculates the differences between these histograms through distance metrics such as Euclidean distance or correlation coefficients. By analyzing the difference values, we obtain similarity scores that quantify the visual resemblance between images. To validate the method's effectiveness, we implemented the complete algorithm in MATLAB programming language, providing source code that includes key functions for image preprocessing, histogram generation, and similarity calculation using built-in functions like imhist() and pdist2() for practical reference.