Name
Abstract | ||
|---|---|---|
As one of the most popular methods for image segmentation, fuzzy C-means algorithm suffers two unavoidable initialization difficulties including obtaining initial cluster centroids and deciding cluster number, which affect the algorithm performance. Motivated by the above, an automatic fuzzy clustering algorithm is proposed in this paper, where observation matrix, judgment matrix and set partitioning are used to select appropriate clustering number automatically. Experimental results show that automatic fuzzy clustering algorithm not only can spontaneously estimate the appropriate number of clusters but also can achieve better segmentation quality. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE 15TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) | Fuzzy clustering, image segmentation, observation matrix, set partitioning |
Field | DocType | ISSN |
Data mining,Fuzzy clustering,CURE data clustering algorithm,Segmentation-based object categorization,FLAME clustering,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Pattern recognition,Correlation clustering,Determining the number of clusters in a data set,Algorithm,Engineering | Conference | 1935-4576 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-ling Li | 1 | 21 | 4.28 |
| Zhiwei Gao | 2 | 796 | 61.68 |
| Xiaoxu Liu | 3 | 67 | 4.72 |