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
Automatic acquisition of visual models for image recognition
ROLI, FABIO;
1992-01-01
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
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