Calculation of Color Moments - Image Processing Feature Extraction Technique

Color Moment calculation represents a fundamental methodology in image processing for feature extraction. Color moments are statistical measures used to characterize the color distribution and features within digital images. This technique involves processing individual pixel color values and computing statistical descriptors including mean, variance, and skewness to represent the image's color characteristics. The mathematical implementation typically involves iterating through image pixels using nested loops and calculating these statistical measures for each color channel (RGB, HSV, etc.). Image processing feature extraction refers to the process of identifying and quantifying representative characteristics that describe image content and properties. These extracted features may encompass information about color patterns, texture properties, shape attributes, and other visual elements. Through systematic feature extraction, professionals can achieve enhanced image understanding and analysis, with applications spanning image processing workflows, computer vision systems, and machine learning algorithms. The implementation often utilizes matrix operations and statistical functions available in libraries like OpenCV or NumPy for efficient computation.