Image search reranking is an effective approach to refining the text-based image search result. In the reranking process, the estimation of visual similarity is critical to the performance. However, the existing measures, based on global or local features, cannot be adapted to different queries. In this paper, we propose to estimate a query aware image similarity by incorporating the global visual similarity, local visual similarity and visual word co-occurrence into an iterative propagation framework. After the propagation, a query aware image similarity combining the advantages of both global and local similarities is achieved and applied to image search reranking. The experiments on a real-world Web image dataset demonstrate that the proposed query aware similarity outperforms the global, local similarity and their linear combination, for image search reranking.
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