Local reinforcement learning for object recognition
Current computer vision systems, whose basic methodology is open-loop or filter type, typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using local reinforcement learning to induce a highly adaptive mapping from input images to segmentation strategies. This is accomplished by using the confidence level of model matching as reinforcement to drive learning. The system is verified through experiments on a large set of real images.