Evaluation of labelling layout methods in augmented reality

View management techniques are commonly used for labelling of objects in augmented reality environments. Combining with image analysis, search space and adaptive representations, they can be utilized to achieve desired labelling tasks. However, the evaluation of different search space methods on labelling are still an open problem. In this paper, we propose an image analysis based view management method, which first adopts the image processing to superimpose 2D labels to the specific object. We then conduct three search space methods to an augmented reality scenario. Without the requirements of setting rules and constraints for occlusion among the labels, the results of three search space methods are evaluated by using objective analysis of related parameters. The evaluation results indicate that different search space methods could generate different time costs and occlusion, thereby affecting the final labelling effects.