Cluster classification characteristics of the critical principal image histogram component

There are a lot of limit image histogram components in any image. This paper selects the principal image histogram components and evaluates them to get the critical one. As the central value of the image the critical principal image histogram component hold clusters and their distributions in the image. On the concept of the pixel distance, determinate mathematical model of probability and cumulative density functions categorize image subclusters and their member details with the threshold of the difference and the threshold of the number of pixels, within the image coverage of the critical principal image histogram component.