As image segmentation technology has continued to develop

scholars have proposed numerous algorithms for image segmentation. Image complexity and structure instability have resulted in a growing number of image segmentation methods

which provide segmentation effects for different types of images. To further improve noise immunity and the accuracy of image segmentation

an improved possibilistic clustering algorithm combining intra-class distance and inter-class distance is proposed and applied to image segmentation. The algorithm uses a possible measure to describe the degree of membership. The constraint that memberships of sample points across clusters must sum to 1 in fuzzy c-means is removed using a possibility measure so that membership degree is suitable for the characterization of "typical" and "compatibility". The algorithm avoids the traditional possibilistic clustering segmentation algorithm that only considers the distance of the sample to the cluster center. In this paper

intra-class and inter-class distances are combined as a new measure for the algorithm

with consideration of both the intra-class compactness and the inter-class scatter degree

to improve the stability and anti-noise ability of different clustering structures. The histogram is integrated into the possibility of the fuzzy clustering segmentation algorithm so that it can achieve segmentation of all types of complex images. Through synthetic and remote sensing images

segmentation tests show that the proposed improved possibilistic clustering algorithm is effective

segmentation contour is clear

and classification accuracy and noise are small. Compared with other algorithms

the error rate is reduced by 2 percentage points

and the result is more satisfactory. This study aims to conduct fuzzy c-means clustering segmentation algorithm and possibilistic clustering segmentation algorithm for image classification with similar backgrounds and targeting color inaccuracy defects by combining intra-class distance and inter-class distance as measures of the algorithm effectively to solve the image segmentation problem classification. This method

combined with the histogram

is proposed for a fast possibilistic fuzzy clustering segmentation algorithm that is also applicable for complex

large images.