Name
Abstract | ||
|---|---|---|
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state genetic algorithm is used to evolve candidate segmented images until a suitable candidate solution is found. To speed up the convergence to an adequate solution, hierarchical local search is used on each evaluated solution. The results show that the hybrid genetic algorithm with hierarchical local search is able to efficiently perform image segmentation. The necessity of hierarchical search for these types of problems is also clearly demonstrated. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330163.2330253 | GECCO |
Keywords | Field | DocType |
hierarchical local search,candidate segmented image,steady-state genetic algorithm,hierarchical search,hybrid genetic algorithm,suitable candidate solution,q-state potts spin glass,adequate solution,grayscale image,image segmentation,genetic algorithms,genetic algorithm,spin glass,local search | Mathematical optimization,Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Local search (optimization),Cultural algorithm,Population-based incremental learning,Grayscale,Machine learning,Genetic algorithm | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Hauschild | 1 | 234 | 10.12 |
| Sanjiv Bhatia | 2 | 13 | 1.33 |
| Martin Pelikan | 3 | 1921 | 130.98 |