Abstract
Abstract This paper explores a Minimum Span Tree (MST) based multi-level 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. On the other hand, a data structure is designed to keep adjacency of the objects during the MST based segmenting process. This make it is a simple and easy way to realize multi-level hierarchy image segmentation. Experiments based on the natural image and high resolution remote sensing images show the proposed merging predicate can keep the integrity of the objects and do well on preventing over-segmentation; the multi-level segmentation can avoid less segmentation. It also proves its efficiency in segmenting the high resolution remote sensing images. It will provide multilevel spatial neighbours’ information for image interpretation.
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