The state of the art in query expansion is mainly based on the spatial information. These methods achieve high performance, however, suffer from huge computation and memory. The objective of this paper is to perform visual reranking in near-real time regardless of the spatial information. We explore a graph-based method proposed as our confident sample detection baseline, which has been proved successful in achieving high precision. In addition, a novel maximum-kernel-based metric function is introduced to rerank the images in the initial result. We evaluated the method on the standard Paris dataset and a new Francelandmark dataset. Our experiments demonstrate that the algorithm has great value on practicality because of its good performance, easy implementation, and high computational efficiency.