Traditional HSV Color Space Feature Extraction Algorithm

The traditional HSV color space feature extraction algorithm converts color information from the RGB space to the HSV space, which better aligns with human intuitive color perception. Here, H (Hue) represents the tonal value, S (Saturation) indicates color purity, and V (Value) corresponds to brightness intensity.

The 8H3S3V quantization method is a common non-uniform quantization strategy implemented as follows:

Hue (H) Quantization: The H component is divided into 8 intervals, each covering 45 degrees within the 0-360 degree range. Since red appears at both ends of the hue circle (near 0° and 360°), special boundary handling is typically required to prevent discontinuity issues. In code implementation, this often involves using modulo operations or conditional checks for H values crossing the circular boundary.

Saturation (S) and Value (V) Quantization: Both S and V are quantized into 3 levels each. This non-uniform division reflects human visual sensitivity, where the eye detects changes more readily in low saturation and low brightness regions. For example: S quantization ranges might be: Low (0-0.2), Medium (0.2-0.7), High (0.7-1) V quantization ranges could be: Dark (0-0.3), Medium (0.3-0.8), Bright (0.8-1) Programmatically, this can be implemented using threshold comparisons or lookup tables for efficient quantization.

Feature Generation: Through the quantized H, S, and V combinations, each pixel is mapped to an 8×3×3=72-dimensional histogram, ultimately forming the image's global color feature. This approach demonstrates certain robustness to lighting variations and is commonly applied in image retrieval or classification tasks. The implementation typically involves iterating through image pixels, computing quantized indices, and accumulating histogram bins.

Potential optimizations include dynamic range adjustment or incorporation of spatial information (such as block-based statistics), but the fundamental 8H3S3V method remains widely used due to its computational simplicity and effectiveness. Code implementations often feature configurable quantization parameters to allow flexibility across different application scenarios.