Image search reranking has become a widely-used approach to significantly boost retrieval performance in the state-of-art content-based image retrieval system. Most of the methods merely rely on matching visual distances between query and initial results or among initial results to detect confident samples relevant to query. However, they may fail to rerank due to the existence of a huge gap between low-level visual features and high-level semantic concepts. In this paper, we propose to detect reliable relevant samples based on a semantic image graph of labeled auxiliary dataset and Markov random walk algorithm. A graph-based rerank method is then presented to propagate the scores of detected confident samples to the rest. Our method is evaluated on the standard Paris dataset and a France dataset introduced by us. The performance is demonstrated to match or exceed the state-of-art.
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