Name
Abstract | ||
|---|---|---|
Conventional fuzzy C-means (FCM) algorithm does not consider spatial information in the clustering, which makes it sensitive to noise and inefficient. In order to overcome these problems, we propose a fast anti-noise FCM algorithm for image segmentation, which constructs a new spatial function by combining pixel gray value similarity and membership. This spatial function is used to update the membership which in turn is used to obtain the cluster centers iteratively. The proposed algorithm can achieve desirable segmentation results in less iterations and reduce the effect of noise effectively. Experimental results show that the proposed algorithm outperforms conventional FCM and other extended FCM algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738562 | 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) |
Keywords | Field | DocType |
Image segmentation, fuzzy clustering, fuzzy C-means, spatial information | Fuzzy clustering,Scale-space segmentation,Pattern recognition,Fuzzy classification,Computer science,Segmentation-based object categorization,Algorithm,Image segmentation,Fuzzy set,Artificial intelligence,FLAME clustering,Cluster analysis | Conference |
ISSN | Citations | PageRank |
1522-4880 | 6 | 0.46 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuhua Zheng | 1 | 6 | 0.46 |
| Caiming Zhang | 2 | 446 | 88.19 |
| Xiaofeng Zhang | 3 | 78 | 8.90 |
| Yi Liu | 4 | 26 | 5.49 |