Similarity Measurement Between Two Images

This implementation focuses on measuring similarity between two images using MATLAB, primarily based on color histogram analysis. In the field of image processing, similarity measurement serves as a fundamental method for comparing and evaluating the degree of resemblance between images. Color histogram represents an effective image feature characterization technique that describes color distribution patterns by statistically analyzing the frequency distribution of various colors within an image. The implementation involves calculating color histograms for both input images using MATLAB's image processing toolbox. Key functions employed include imhist() for histogram computation and pdist2() or corr2() for similarity comparison between histograms. The algorithm typically follows these steps: convert images to appropriate color space (RGB/HSV), compute normalized histograms for each channel, and apply similarity metrics such as Euclidean distance, correlation coefficient, or histogram intersection to quantify the resemblance. This method finds applications in various image processing tasks including image retrieval, image classification, and image matching. By implementing this similarity measurement approach, researchers can better understand and analyze relationships between images, thereby expanding possibilities in image processing and computer vision research. The code structure typically involves histogram normalization to ensure scale invariance and may incorporate multiple color spaces for enhanced robustness.