Semi-Supervised Image Segmentation Algorithm Based on Region Fusion

This paragraph provides a detailed explanation of the semi-supervised image segmentation algorithm based on region fusion. The implementation begins with manual initialization of foreground and background segmentation markers, which serve as seed points for subsequent processing. During iterative operations, the algorithm continuously performs region fusion by calculating similarity metrics (typically using color, texture, or boundary strength features) and merging adjacent regions with maximum affinity. The core algorithm logic involves maintaining a priority queue for efficient region merging, where regions are processed in descending order of similarity. This iterative optimization process enables precise target segmentation by progressively refining region boundaries through fusion operations. The key innovation lies in using region fusion to dynamically optimize segmentation results, significantly improving image segmentation accuracy compared to conventional methods.