Name
Abstract | ||
|---|---|---|
Image segmentation is the basis of image analysis, object tracking, and other fields. However, image segmentation is still a bottleneck due to the complexity of images. In recent years, fuzzy clustering is one of the most important selections for image segmentation, which can retain information as much as possible. However, fuzzy clustering algorithms are sensitive to image artifacts. In this study, an improved image segmentation algorithm based on patch-weighted distance and fuzzy clustering is proposed, which can be divided into two steps. First, the pixel correlation between adjacent pixels is retrieved based on patch-weighted distance, and then the pixel correlation is used to replace the influence of neighboring information in fuzzy algorithms, thereby enhancing the robustness. Experiments on simulated, natural and medical images illustrate that the proposed schema outperforms other fuzzy clustering algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-019-08041-x | Multimedia Tools and Applications |
Keywords | Field | DocType |
Fuzzy clustering, Image segmentation, Patch-weighted distance, Pixel correlation | Computer vision,Fuzzy clustering,Bottleneck,Weighted distance,Pattern recognition,Computer science,Fuzzy logic,Image segmentation,Robustness (computer science),Video tracking,Artificial intelligence,Pixel | Journal |
Volume | Issue | ISSN |
79 | 1 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofeng Zhang | 1 | 44 | 4.84 |
| Muwei Jian | 2 | 235 | 30.97 |
| Yujuan Sun | 3 | 16 | 3.96 |
| Hua Wang | 4 | 0 | 0.34 |
| Caiming Zhang | 5 | 446 | 88.19 |