Image Retrieval Source Code Based on Color Features

This MATLAB implementation creates an image retrieval system based on color features. The core methodology involves extracting color distribution information as feature vectors, then calculating similarity between query images and database images to rank results. Here's a detailed breakdown of key implementation logic:

Color Feature Extraction Typically uses color histograms as feature representations. HSV color space is more suitable for retrieval tasks than RGB since it separates hue (H) from brightness (V). Implementation requires converting images to HSV space, performing non-uniform quantization on the H channel (e.g., dividing 0-360° into 16 levels), then counting frequency occurrences of pixel values to form histogram vectors. Code approach: Use rgb2hsv() for conversion, implement quantization via floor(h_value/22.5) for 16 bins, then histcounts() for frequency calculation.

Feature Normalization Histograms must be normalized into probability distributions (summing to 1) to eliminate size variations. Gaussian smoothing can be applied to reduce noise interference. Implementation: Normalize using hist_vector/sum(hist_vector), apply imgaussfilt() for smoothing with sigma=0.5-1.0.

Similarity Measurement Database image features need pre-extraction and storage. During retrieval, similarity is measured by calculating distances between histograms using methods like: Histogram Intersection: Sum minimum values of corresponding bins - higher values indicate greater similarity Chi-Square Distance: Penalizes bins with larger differences more heavily Bhattacharyya Coefficient: Evaluates similarity through probability distribution overlap MATLAB functions: Implement intersect = sum(min(hist1,hist2)), chi2 = sum((hist1-hist2).^2./(hist1+hist2+eps))

Result Ranking and Display Sort database images in descending order by similarity scores and visualize top N results. Add progress bars to indicate retrieval time for better user experience. Code enhancement: Use sort() with 'descend' option, implement waitbar() for progress indication, display results using montage() or subplot().

Extension Considerations Multi-feature Fusion: Combine with texture (LBP) or shape features to improve accuracy Spatial Information: Extract histograms from image blocks to preserve color distribution relationships Retrieval Optimization: Use KD-trees or hashing methods to accelerate large-scale database searches Advanced implementation: Consider using fitckdtree() for KD-tree construction or implement locality-sensitive hashing.