Computer vision can highly benefit from modern learning methods. In the context of an active vision environment we introduce a machine learning approach which is able to learn strategies of object acquisition. We propose a hybrid learning method, called Sphinx, that combines two approaches originating from seperate disciplines of computer science, namely reinforcement learning on the one hand and belief revision on the other. The former represents knowledge in a numerical way, while the latter is based on symbolic logic and allows reasoning. Sphinx is designed according to human cognition and interacts with its environment by rotating objects depending on past perceptions to acquire those views which are advantageous for recognition. Our method was successfully applied in simulations of object categorization tasks.
Abstract:We propose an active vision system for the acquisition of internal object representations. The core of the approach is an agent which learns goal-directed action patterns depending on the perceived environment via reinforcement learning. The user supervision is restricted to the definition of this goal in form of a reward function. We demonstrate this approach by means of learning a strategy to scan an object. The agent moves a virtual camera around an object and is able to adapt her scan path dynamically to different conditions of the environment such as different objects and different goals of the data acquisition. The purpose of the acquisition we consider here is the view-based reconstruction of nonacquired views. The scan pattern obtained after the learned path has stabilized allows a better reconstruction of unfamiliar views than random scan paths.
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