Name
Abstract | ||
|---|---|---|
In this paper, we present an efficient unsupervised mixture model image segmentation method. The idea of this method is that individual image region classes are modeled as mixtures of fuzzy subclasses of mixture distributions, and classification is performed based on the Expectation-Maximization algorithm. To overcome the difficulty of classical mixture model method for noisy image segmentation, spatial contextual information should be taken into account. In particular, the proposed approach based on Markov Random Field was shown to provide more accurate classification of images than traditional Expectation-Maximization algorithm and traditional Markov Random Field image segmentation techniques. The effectiveness of the proposed method is illustrated with synthetic and real images data. The experiments results have shown that the proposed method can achieve more robust segmentation for noisy images. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893257_42 | ICONIP |
Keywords | Field | DocType |
image segmentation,expectation maximization algorithm,mixture distribution,mixture model | Scale-space segmentation,Pattern recognition,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Contextual image classification,Mixture model,Minimum spanning tree-based segmentation,Machine learning | Conference |
Volume | Issue | ISSN |
4233 LNCS - II | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-46481-6 | 1 | 0.35 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pan Lin | 1 | 1 | 0.69 |
| Xiaojian Zheng | 2 | 1 | 0.69 |
| Gang Yu | 3 | 4 | 2.15 |
| ZuMao Weng | 4 | 1 | 0.35 |
| Shengzhen Cai | 5 | 10 | 1.68 |