Recently image search engines mainly base on associated textual information. Image reranking is an effective approach to refine the initial text-based search result by mining the visual information of the returned images. And the estimation of visual similarity is the fundamental factor in reranking methods. However, the existing similarity measures are independent of the query. This paper proposes a query dependent method by incorporating the global visual similarity, local visual similarity and visual word co-occurrence into an iterative propagation framework. Then it embed the query dependent similarity into random walk rereanking method. The experiments on a collected Live Image dataset demonstrate that the proposed query dependent similarity outperforms the global, local similarity and their linear combination.