Extraction of Multiple Parameter Features Required for Histograms

In the field of image processing, histogram analysis serves as a fundamental yet powerful tool that reveals statistical characteristics of images. By calculating various parameter features of histograms, we can gain deeper insights into the distribution patterns of images. This MATLAB program implements feature extraction through histogram calculation functions and statistical operations, retrieving multiple key histogram features including mean, variance, skewness, kurtosis, energy, and entropy.

The mean reflects the average intensity of pixel values, serving as an indicator of the central position of the histogram. The variance measures the dispersion degree of pixel value distribution - larger values indicate more dramatic pixel value variations. Skewness describes the symmetry of the distribution, where positive skewness indicates a long tail on the right side, while negative skewness shows the opposite pattern. Kurtosis characterizes the sharpness of the distribution, with high kurtosis indicating concentrated distribution and low kurtosis representing relatively flat distribution.

Energy is a measure of the sum of squared pixel values, reflecting the overall intensity of the image. Entropy quantifies the information content of the histogram - higher entropy values indicate more random distribution of pixel values and richer information content. These features find wide applications in tasks such as image classification, quality assessment, and pattern recognition, providing crucial foundations for subsequent analysis. The implementation utilizes MATLAB's built-in functions like mean(), var(), skewness(), and kurtosis() for statistical calculations, while energy and entropy are computed through custom algorithms based on probability distributions.