A Faster Graph-Based Segmentation Algorithm with Statistical Region Merge

The paper presents a modification of a bottom up graph theoretic image segmentation algorithm to improve its performance. This algorithm uses Kruskal’s algorithm to build minimum spanning trees for segmentation that reflect global properties of the image: a predicate is defined for measuring the evidence of a boundary between two regions and the algorithm makes greedy decisions to produce the final segmentation. We modify the algorithm by reducing the number of edges required for sorting based on two criteria. We also show that the algorithm produces an over segmented result and suggest a statistical region merge process that will reduce the over segmentation. We have evaluated the algorithm by segmenting various video clips Our experimental results indicate the improved performance and quality of segmentation.