MATLAB Mini-Application for Content-Based Image Retrieval Using Color Features

This MATLAB mini-application implements content-based image retrieval using 9 dominant colors as primary features. The program enables users to find visually similar images by analyzing an input query image. Through color feature extraction and comparison algorithms, it identifies images with similar color distributions from the database and returns ranked results to users. The implementation involves key processes including color space conversion, dominant color extraction using clustering algorithms (typically k-means with k=9), and similarity calculation through histogram intersection or Euclidean distance methods. The application generates color histograms representing the distribution of 9 predominant colors in each image, then computes similarity scores between query and database images. This tool has practical applications in image search systems, visual recognition tasks, and digital asset management. For users requiring rapid identification of visually similar images, this application serves as an efficient solution with straightforward MATLAB implementation using image processing toolbox functions like imread, rgb2lab for color space conversion, and kmeans for color clustering. The code structure typically includes modules for feature extraction, database indexing, and similarity matching operations.