Large scale region-merging segmentation using the local mutual best fitting concept

Large scale segmentation remains a challenging task because of time and memory consuming. A usual strategy to process efficiently a large volume of data is to divide into chunks to be processed separately, either sequentially to reduce memory footprint or in parallel in order to speed up the computation. In image processing in general this boils down to dividing the input image into tiles. However, for image segmentation, the tile splitting usually leads incoherent segments on the borders of the tiles even when some overlap between the tiles is applied. In this paper we propose a new strategy making possible the tiling for image segmentation algorithms while maintaining the accuracy of the final results. Specifically, we focus on iterative region merging methods but the strategy can be extended to any segmentation algorithm. The introduction of the local mutual best fitting concept and the area of influence of a segment allows to establish a new methodology of segmentation based on three phases: the tile-based reduction, the iterative reduction and the completion of the segmentation. This new methodology was applied on a large Pleiades HR image with success proving the feasibility of the approach.