Name
Abstract | ||
|---|---|---|
This paper presents an unsupervised segmentation algorithm for color images. The algorithm consists of two stages. In the first stage, the optimal number of segments is automatically determined by means of a compactness measure that is formulated to find a clustering with "maximum inter-cluster distance and minimum intra-cluster variance". In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function and the image is segmented. A large number of performance evaluations have been carried out and the experimental results indicate that the proposed approach is effective, and it obtains satisfied results in comparing with other algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISM.2012.100 | ISM |
Keywords | Field | DocType |
minimum intra-cluster variance,energy function,pattern clustering,unsupervised segmentation algorithm,terminal vertices weighted graph,image segmentation,minimum intracluster variance,color image,large number,k-means,multiple terminal,graph cut,unsupervised color image segmentation,graph theory,unsupervised color image,optimal number,compactness measure,maximum inter-cluster distance,maximum intercluster distance,image colour analysis,clustering,graph cut based unsupervised color image segmentation algorithm | Cut,Computer vision,k-means clustering,Scale-space segmentation,Pattern recognition,Computer science,Image texture,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Connected-component labeling,Minimum spanning tree-based segmentation | Conference |
ISBN | Citations | PageRank |
978-1-4673-4370-1 | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Bin-mei | 1 | 0 | 0.34 |
| Jian-Zhou Zhang | 2 | 22 | 5.38 |