2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China;

3. Information and Navigation Institute of Air Force Engineering University, Xi'an, Shaanxi 710077, China

**Overview**: Image segmentation is the first step in image processing, and plays an important role in image subsequent processing. The quality of feature extraction, target recognition and target detection all depend on the effect of image segmentation. Image segmentation has become a research hotspot and difficulty due to the changes of illumination and scale, the effects of noise and the problems of image itself. At present, image segmentation algorithms mainly include region-based segmentation algorithm, edge-based segmentation algorithm, threshold-based segmentation algorithm, and clustering-based segmentation algorithm. The watershed segmentation algorithm is a typical algorithm based on region segmentation. It has the characteristics of simple implementation, good performance and strong contour extraction ability, but the image over-segmentation problem is more serious. The SLIC algorithm is a super-pixel segmentation algorithm based on gradient rise. It has a faster processing speed, and the super-pixel block can fit the boundary of the target well, and can obtain super-pixel blocks with the same shape and size, but cannot segment the target area, which increases the difficulty for subsequent processing. In order to solve the over-segmentation problem caused by the traditional watershed segmentation algorithm and other existing algorithms(large data processing capacity and low operation efficiency), as well as the problem that the SLIC cannot segment the target region, an image segmentation algorithm based on SLIC algorithm and watershed algorithm is proposed. Firstly, a method of calculating the number of super pixels in the SLIC algorithm is proposed, which used the image complexity and image size to calculate the number of super-pixels pre-segmented, and then used the SLIC segmentation method to pre-process the original image for super-pixel segmentation to reduce redundant information in subsequent processing; Then, a method of adaptively calculating the threshold using mean and variance was proposed to perform threshold processing on the gradient image of the image to effectively remove noise and obtain more complete contour information. Finally, the image was extracted from the minimum value mark to obtain the marked image, and the image was segmented by the watershed segmentation algorithm to obtain the final segmentation image. The algorithm can effectively solve the over-segmentation problem generated by the traditional watershed algorithm. Through the statistical analysis experiment of 500 images in the Berkeley database, and the real local consistency error rate and global consistency error of 100 images and the ground truth are calculated. The fractional rate is eventually found to be better than the traditional algorithms and other marking algorithms, and the ideal segmentation effect is obtained.