Multi-graph similarity reinforcement for image annotation refinement

In image annotation refinement, word correlations among candidate annotations are used to reserve high relevant words and remove irrelevant words. Existing methods build word correlations on textual annotations of images. In this paper, visual contents of images are utilized to explore better word correlations by using multi-graph similarity reinforcement method. Firstly, image visual similarity graph and word correlations graph are built respectively. Secondly, the two graphs are iteratively reinforced by each other through image-word transfer matrix. Once the two graphs converge to steady states, the new word correlations graph is used to refine the candidate annotations. The experiments show that our method performs better than method not considering visual content of images.