Graph cut and image segmentation using mean cut by means of an agglomerative algorithm

Graph partitioning, or graph cut, has been studied by several authors as a tool for image segmentation. It refers to partitioning a graph into several subgraphs such that each of them represents a meaningful object of interest in the image. In this work we propose a hierarchical agglomerative clustering algorithm driven by the cut and mean cut criteria. Some preliminary experiments were performed using the benchmark of Berkeley BSDS500 with promising results.