Dam Burst: A region-merging-based image segmentation method
This addresses the issue of over-segmentation in image segmentation for computer vision applications, but it appears incremental as it builds on existing region-merging techniques.
The paper tackles the problem of over-segmentation in single-level image segmentation algorithms by proposing a region-merging-based method called Dam Burst, which avoids over-segmentation while retaining details by simulating flooding and using edge detection to strengthen dam structures.
Until now, all single level segmentation algorithms except CNN-based ones lead to over segmentation. And CNN-based segmentation algorithms have their own problems. To avoid over segmentation, multiple thresholds of criteria are adopted in region merging process to produce hierarchical segmentation results. However, there still has extreme over segmentation in the low level of the hierarchy, and outstanding tiny objects are merged to their large adjacencies in the high level of the hierarchy. This paper proposes a region-merging-based image segmentation method that we call it Dam Burst. As a single level segmentation algorithm, this method avoids over segmentation and retains details by the same time. It is named because of that it simulates a flooding from underground destroys dams between water-pools. We treat edge detection results as strengthening structure of a dam if it is on the dam. To simulate a flooding from underground, regions are merged by ascending order of the average gra-dient inside the region.