Name
Abstract | ||
|---|---|---|
Unsupervised segmentation evaluation method quantifies the quality of segmentation without the reference segmentation or user assistance. Although some methods have been proposed to statistically analyze the pixel values, these methods are not sensitive enough to provide a metric of segmentation quality. This paper uses the image edge, a more robust feature, to measure the quality of segmentation. An edge-based segmentation evaluation method is introduced in this paper, which can be applied to both image and single region segmentation evaluation. The proposed method evaluates the quality of segmentation with three edge-based measures: the edge fitness, the intra-region edge error, and the out-of-bound error. These measures encourage the outline of segmentation to align with the edge and punish the segmentation that exceeds the edge. Experiments results show that our method is more sensitive to under-segmentation and over-segmentation. Using the parameters optimized by the proposed method, the segmentation produced by the classic region growing method is visually similar to the state-of-the-art segmentation method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-3542-8 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Image segmentation, Objective evaluation, Unsupervised evaluation | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Pixel,Region growing,Minimum spanning tree-based segmentation | Journal |
Volume | Issue | ISSN |
76 | 8 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| zhaoquan cai | 1 | 19 | 4.37 |
| Yihui Liang | 2 | 8 | 4.16 |
| Han Huang | 3 | 159 | 30.23 |