Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Software agent with reinforcement learning approach for medical image segmentation
Journal of Computer Science and Technology
Learning to segment document images
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Object recognition is a multilevel process requiring a sequence of algorithms at low, intermediate, and high levels. Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision and pattern recognition research. A robust closed-loop system based on “delayed” reinforcement learning is introduced. The parameters of a multilevel system employed for model-based object recognition are learned. The method improves recognition results over time by using the output at the highest level as feedback for the learning system. It has been experimentally validated by learning the parameters of image segmentation and feature extraction and thereby recognizing 2D objects. The approach systematically controls feedback in a multilevel vision system and shows promise in approaching a long-standing problem in the field of computer vision and pattern recognition