Delayed reinforcement learning for adaptive image segmentation and feature extraction

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 in this paper. 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 2-D objects. The approach systematically controls feedback in a multilevel vision system and shows promise in approaching a long-standing problem in the fleld of computer vision and pattern recognition.