To address the issues of low segmentation accuracy, low efficiency, and unstable segmentation results with increasing thresholds in color image multi-threshold segmentation, an improved multi-threshold image segmentation algorithm based on the improved zebra optimization algorithm (IZOA) is proposed. Firstly, a chaotic mapping method is used to initialize the population; secondly, a neighborhood fluctuation strategy is introduced for fine searching; then, hybridization and mutation operations are combined to generate new solutions, enhancing the global search capability of the algorithm; finally, an elite retention strategy is employed to preserve the optimal solution. The minimum symmetric cross-entropy obtained before and after image segmentation is utilized as the fitness function for multi-threshold segmentation, demonstrating higher segmentation accuracy, efficiency, and stability. Experimental results show that compared with ZOA, GWO, WOA, and other algorithms, the image quality indices FSIM, SSIM, and PSNR achieved by the IZOA-based segmentation exhibit significant advantages, with the optimal truncation mean proportions reaching 91.7%, 88.9% and 100%, respectively.