Extracting Color Moments in RGB Color Space with Algorithmic Implementation

Color moments are essential statistical features for describing image color distributions, particularly widely applied in RGB color space. By calculating low-order moments (including first, second, and third moments) of image RGB channels, we can effectively represent color characteristics of images. In implementation, these moments are typically computed using array operations on pixel values across each color channel. First Moment (Mean) The first moment reflects the average color intensity across RGB channels, calculated as the mean value of pixel intensities for each channel. This feature intuitively represents the overall tone of an image. Code implementation involves summing all pixel values per channel and dividing by the total pixel count using vectorized operations for efficiency. Second Moment (Variance) The second moment measures the dispersion degree of color distribution in each channel, indicating the range of color variations. Images with larger variance typically exhibit higher contrast or richer color diversity. Algorithm implementation requires calculating the squared differences from the mean, often optimized using statistical functions in image processing libraries. Third Moment (Skewness) The third moment describes the symmetry of color distribution, reflecting the skewness of image color distribution. Positive skewness indicates color distribution biased toward high-intensity regions, while negative skewness shows bias toward low-intensity areas. Computational methods involve normalized third-order central moments, typically implemented using specialized statistical functions. Color moments are highly practical for image retrieval and matching due to their computational simplicity and effective characterization of global color distribution. Compared to histograms, they require less storage space, making them suitable for rapid comparison in large-scale image databases. In practical applications, combining color moments with other features (such as texture and shape descriptors) can further enhance retrieval accuracy. Implementation often involves feature concatenation and distance metric calculations for similarity assessment.