Traditional HSV Color Space Feature Extraction Algorithm Implementation

In this article, we delve into the implementation details of the traditional HSV color space feature extraction algorithm. This algorithm quantizes image color information into 8 Hue values, 3 Saturation levels, and 3 Value (brightness) ranges, facilitating efficient computer processing and analysis. The quantization process typically involves creating a 3D histogram (8×3×3 = 72 bins) where each pixel's HSV values are mapped to corresponding bins based on predefined thresholds. The core implementation involves converting RGB images to HSV color space using mathematical transformations, followed by discrete quantization where: - Hue (0-360°) is divided into 8 segments (45° intervals) - Saturation and Value (0-1) are each partitioned into 3 equal ranges This creates a compact feature vector representing the color distribution. While this method has been widely adopted in image processing applications, technological advancements have prompted the development of more efficient and accurate color feature extraction techniques. Consequently, we aim to enhance the traditional algorithm by incorporating additional feature extraction modalities, such as spatial distribution analysis and adaptive quantization schemes, to better address diverse application requirements. Through these improvements, we seek to contribute significantly to both research and practical implementations in the image processing domain.