A Image Segmentation Method Based on Statistics Learning Theory and Minimum Spanning Tree

According to the essential feature of object-oriented image segmentation method, this paper explores a minimum span tree (MST) based image segmentation method. We define an edge weight based optimal criterion (merging predicate) which based on statistical learning theory (SLT), a scale control parameter is used to control the segmentation scale. Experiments based on the high resolution UAV images show that the proposed merging predicate can keep the integrity of the objects and do well on preventing over segmentation. It also proves its efficiency in segmenting the rich texture images while can get good boundary of the object.