Name
Abstract | ||
|---|---|---|
We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical information, such as local probability distribution, orientation, and anisotropy. The combined information is used to design an adaptive local classification strategy that improves the robustness of the algorithm and also keeps fine features in the image. The proposed methodology is flexible and can be easily generalized to deal with other inferred information/quantities and statistical methods/models. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1137/080731530 | SIAM J. Imaging Sciences |
Keywords | Field | DocType |
local adaptivity,expectation-maximization algorithm,combined information,local probability distribution,geometrical information,adaptive local classification strategy,statistical method,local statistic,proper statistical model,image segmentation,posterior probability,gaussian mixture model,expectation maximization algorithm | Mathematical optimization,Pattern recognition,Expectation–maximization algorithm,Posterior probability,Image segmentation,Robustness (computer science),Probability distribution,Statistical model,Artificial intelligence,Mixture model,Mathematics,Global statistics | Journal |
Volume | Issue | ISSN |
2 | 3 | 1936-4954 |
Citations | PageRank | References |
3 | 0.39 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingyu Leung | 1 | 164 | 18.35 |
| Gang Liang | 2 | 27 | 2.02 |
| Knut Sølna | 3 | 142 | 46.02 |
| Hongkai Zhao | 4 | 797 | 74.83 |