Name
Abstract | ||
|---|---|---|
A novel stochastic searching scheme based on the Monte Carlo optimization is presented for polygonal approximation (PA) problem. We propose to combine the split-and-merge based local optimization and the Monte Carlo sampling, to give an efficient stochastic optimization scheme. Our approach, in essence, is a well-designed Basin-Hopping scheme, which performs stochastic hopping among the reduced energy peaks. Experiment results on various benchmarks show that our method achieves high-quality solutions with lower computational costs, and outperforms most of state-of-the-art algorithms for PA problem. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.857 | ICPR |
Keywords | Field | DocType |
efficient stochastic optimization scheme,well-designed basin-hopping scheme,efficient polygonal approximation,monte carlo optimization,pa problem,experiment result,digital curves,monte carlo sampling,high-quality solution,novel stochastic,lower computational cost,local optimization,monte carlo,merging,stochastic optimization,sampling methods,optimization,stochastic processes,approximation algorithms,computational geometry,monte carlo methods | Stochastic optimization,Computer science,Artificial intelligence,Monte Carlo integration,Stochastic tunneling,Monte Carlo method,Mathematical optimization,Pattern recognition,Global optimization,Algorithm,Hybrid Monte Carlo,Dynamic Monte Carlo method,Monte Carlo molecular modeling | Conference |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuzhuang Zhou | 1 | 380 | 20.26 |
| Yao Lu | 2 | 98 | 19.25 |