Split-and-merge segmentation employing thresholding technique

The conventional split-and-merge algorithm is lacking in the adaptability to the image semantics because of its stiff quadtree-based structure. In this paper, a thresholding technique is employed in the splitting phase of the split-and-merge segmentation scheme to directly reflect the image semantics to the image segmentation results. Thus, the regions which contain distinct subregions can be extracted by one step, which significantly alleviating the computation load and the memory requirements. To overcome the problems, caused by the block-based thresholding, such as edge discontinuities over the borders of quadtree nodes, a prefilter is used to preserve and sharpen the edge information. Applying the proposed algorithm to aerial images results in the reduction of the number of intermediate regions generated in the splitting phase, demonstrating the improved ability to capture the image semantics. The simulation results shows that the proposed algorithm successfully extract arbitrary shaped regions in aerial images.