Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

• Fusion of Probabilistic knowledge-based classification rules and learning automata.
• The rules probabilities change guided by a supervised reinforcement process.
• Automatic recognition of images corresponding to visual landmarks for UAVs.
• Comparison with well-established pattern recognition methods.

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.