Abstract: The fuzzy c-means (FCM) clustering algorithm has become a popular choice among many scholars for image segmentation due to its simplicity in implementation and alignment with practical scenarios. However, the traditional FCM algorithm has a drawback:Random initialization of cluster centers. To appropriately select cluster centers, this paper proposes a hybrid multi-strategy sparrow search algorithm (SSA). By leveraging the strong optimization capability of the SSA, the initial cluster centers of the FCM algorithm are optimized to enhance its segmentation performance. The algorithm’s approach is as follows: First, to address the deterioration of population diversity in the later stages of the SSA, the Fuch chaotic map is introduced. To mitigate the tendency of the sparrow population to oscillate around local extrema, pinhole imaging opposition-based learning is employed to update the positions of discoverers. Additionally, to improve the global search capability of the sparrow population, GaussCauchy mutation is introduced to update the positions of followers. Ultimately, an improved SSA with enhanced optimization accuracy and speed is obtained. The objective function of the FCM algorithm is used as the optimization function of the improved SSA for natural scene and cell image segmentation experiments. Compared to the standard FCM algorithm, the proposed algorithm demonstrates an approximately 5 percentage point improvement in the average partition coefficient and enhanced robustness.

Key words: sparrow search algorithm, reverse learning, GaussCauchy variation, fuzzy c-means (FCM), image segmentation