Name
Title | ||
|---|---|---|
Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination. |
Abstract | ||
|---|---|---|
A class of multiscale stochastic models based on scale-recursive dynamics on trees has previously been introduced. Theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale, e.g., 1/f processes, as well as 1D Markov processes and 2D Markov random fields. Moreover, efficient optimal estimation algorithms have been developed for these models by exploiting their scale-recursive structure. The authors exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation. They illustrate one possible application to texture discrimination and demonstrate that likelihood-based methods using the algorithm achieve performance comparable to that of Gaussian Markov random field based techniques, which in general are prohibitively complex computationally. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/83.342185 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
optimal estimation algorithms,likelihood calculation,parallelizable algorithm,stochastic processes,scale-recursive dynamics,texture discrimination,computational complexity,image classification,multiscale stochastic models,image texture | Markov process,Artificial intelligence,Random field,Likelihood function,Pattern recognition,Markov model,Markov chain,Stochastic process,Algorithm,Stochastic modelling,Variable-order Markov model,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 2 | 1057-7149 |
Citations | PageRank | References |
35 | 5.27 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. R. Luettgen | 1 | 125 | 24.52 |
| Alan S. Willsky | 2 | 7466 | 847.01 |