Name
Abstract | ||
|---|---|---|
Knowledge-based image recognition offers numerous advantages, including powerful knowledge representation and comprehensibility of recognition criteria, but exhibits the drawback of a difficult knowledge-acquisition process. To overcome such a drawback, the paper presents a learning system for automatic generation of descriptions of objects to be recognized in 2D images. First, the authors analyze the importance of adopting a framework for the definition and use of relational descriptions. Then, the authors present the system obtained by making such a framework utilize the learning methodology proposed by R. Michalski (1980) for INDUCE. The authors have specialized this methodology in order to cope with image recognition problems. A quantitative performance assessment is reported, as well as comparisons with decision trees and with the k-nearest neighbours algorithm |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1109/ICPR.1992.201516 | The Hague |
Keywords | DocType | Citations |
fuzzy logic,knowledge based systems,learning systems,pattern recognition,2d images,induce,decision trees,k-nearest neighbours algorithm,knowledge representation,knowledge-based image recognition,learning system,quantitative performance assessment,relational descriptions,visual models,image recognition,knowledge base,decision tree | Conference | 3 |
PageRank | References | Authors |
0.54 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fichera, O. | 1 | 3 | 0.54 |
| Pellegretti, P. | 2 | 3 | 0.54 |
| Fabio Roli | 3 | 4846 | 311.69 |
| Serpico, S.B. | 4 | 560 | 48.52 |