Abstract: Image segmentation is the research hotspot and difficulty in computer vision. Based on local information, the
fuzzy local information C-means (FLICM) clustering algorithm improves the robustness of the algorithm to a certain
extent, but cannot attain the expected image segmentation effect in the case of high noise intensity. Aiming at the low
segmentation accuracy of traditional fuzzy clustering algorithm, an improved image segmentation algorithm based on
pixel correlation model was proposed. Firstly, a new pixel correlation model was designed by analyzing the local
statistical characteristics of pixels. On this basis, non-local information was effectively employed to mine the details in
the image and improve the image segmentation effect. In the experiment, a variety of evaluation indexes were used to
evaluate the segmentation results, and compared with a variety of common fuzzy clustering algorithms. Experimental
results show that the fuzzy clustering algorithm based on improved pixel correlation can effectively balance the degree of
resistance to noise and the degree of retention of image details in synthetic images, natural images, medical images, and
remote sensing images, and that the segmentation effect and robustness are superior to the correlation algorithm.

Key words: image segmentation, local statistical characteristics, pixel correlation, nonlocal information