Automatic image annotation using community detection in neighbor images

Automatic image annotation is useful in automatic image management and, understanding their contents. Automatic image annotation refers to the process of assigning tags/labels to images so that they clearly indicate the content which is meant to be conveyed. In this paper, a new method of automatic image annotation is presented. In the first phase of the proposed method, neighbor images which are similar to the query image, are retrieved using low-level features. In the second phase, a network (a relative graph) of the tags neighbor images is created and, finally, the tags of the densest community among all communities is selected for the query image as final tags. In order to show the efficiency of the proposed method some metrics such as precision, recall, and f-score are used in three standard datasets namely Corel5k, IAPR TC12 and Mir Flickr. The results show that the proposed method is more efficient than some state-of-the-art approaches.