Name
Abstract | ||
|---|---|---|
Image classification and clustering is a challenging problem in computer vision. This paper proposed a kind of particle swarm optimization clustering approach: FPSOC to process image clustering problem. This approach considers each particle as a candidate cluster center. The particles fly in the solution space to search suitable cluster centers. This method is different from previous work in that it employs fuzzy concept in particle swarm optimization clustering and adopts attribute selection mechanism to avoid the ‘curse of dimensionality’ problem. The experimental results show that the presented approach can properly process image clustering problem. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11922162_53 | PCM |
Keywords | Field | DocType |
particle swarm optimization,process image,image clustering,particle swarm optimization clustering,fuzzy particle swarm optimization,computer vision,attribute selection mechanism,challenging problem,suitable cluster center,candidate cluster center,image classification,attribute selection,curse of dimensionality | Fuzzy clustering,Data mining,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Multi-swarm optimization,Artificial intelligence,FLAME clustering,Cluster analysis | Conference |
Volume | ISSN | ISBN |
4261 | 0302-9743 | 3-540-48766-2 |
Citations | PageRank | References |
1 | 0.37 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wensheng Yi | 1 | 11 | 1.70 |
| Min Yao | 2 | 9 | 1.66 |
| Zhiwei Jiang | 3 | 41 | 6.41 |