Image annotation plays an important role in image retrieval and management. However, the results of the state-of-the-art image annotation methods are often unsatisfactory. Therefore, it is necessary to refine the imprecise annotations obtained by existing annotation methods. In this paper, a novel approach to automatically refine the original annotations of images is proposed. On the one hand, for Web images, textual information, e.g. file name and surrounding text, is used to retrieve a set of candidate annotations. On the other hand, for non-Web images that are lack of textual information, a relevance model-based algorithm using visual information is used to decide the candidate annotations. Then, candidate annotations are re-ranked and only the top ones are reserved as the final annotations. To re-rank the annotations, an algorithm using Random Walk with Restarts (RWR) is proposed to leverage both the corpus information and the original confidence information of the annotations. Experimental results on both non-Web images of Corel dataset and Web images of photo forum sites demonstrate the effectiveness of the proposed method.