Content-Based Image Retrieval Using MATLAB

Implementing content-based image retrieval (CBIR) using MATLAB involves the following key steps: 1. Image Preprocessing: Preprocess input images through operations like resizing, noise reduction, and contrast enhancement. In MATLAB, this can be achieved using functions like imresize for dimension adjustment, medfilt2 for median filtering, and imadjust for contrast enhancement. 2. Feature Extraction: Extract distinctive features from images using MATLAB's image processing toolbox. Common features include color histograms (using imhist and rgb2hsv functions), texture features (through gray-level co-occurrence matrix via graycomatrix), and shape descriptors (employing regionprops for geometric properties). 3. Similarity Calculation: Compare query image features with database images using distance metrics. MATLAB implementations typically use pdist2 for Euclidean distance calculation or corr2 for correlation-based matching between feature vectors. Feature databases can be organized using tables or structures for efficient indexing. 4. Result Presentation: Display top matching images based on similarity rankings. This can be implemented using montage function for grid display or subplot arrangements, with similarity scores annotated using text or title functions. Through these MATLAB-implemented steps, content-based image retrieval enables users to accurately locate desired images by leveraging visual content similarities rather than textual metadata. The system architecture supports scalable feature database management through MATLAB's data structures and optimized image processing algorithms.