Fast block-based image segmentation for natural and texture images

The block-based image segmentation method is known to alleviate the over-segmentation problem of the morphological segmentation methods. In this paper, we improve the previous block-based MAP segmentations. First, to reduce the execution time, we try to reduce the number of undecided blocks. That is, as the block size is reduced, we define new monotone regions with the undecided blocks to decrease the number of undecided blocks and to overcome the under-segmentation problem. Second, to improve the segmentation accuracy, we adopt two different block sizes. For texture block clustering process, we use a large block-size. On the contrary, for monotone and edge block classification, it is more efficient to use a small block-size. The proposed segmentation method is applied to natural images with monotone and texture regions. Experimental results show that the proposed method yields large segments for texture regions while it can also pick up some detail monotone regions to overcome the under-segmentation problem.