Image reranking is an effective post-processing step to adjust the similarity order in image retrieval. As key components of initialized ranking lists, top-ranked neighborhoods of a given query usually play important roles in constructing dissimilarity measure. However, the number of pertinent candidates varies with respect to different queries. Thus the images with short lists of ground truth suffer from insufficient contextual information. It consequently introduces noises when using k-nearest neighbor rule to define the context. In order to alleviate this problem, this paper proposes auxiliary points which are added as assistant neighbors in an unsupervised manner. These extra points act on revealing implicit similarity in the metric space and clustering matched image pairs. By isometrically embedding each constructed metric space into the Euclidean space, the image relationships on underlying topological manifolds are locally represented by distance descriptions. Furthermore, by combining Jaccard index with our auxiliary points, we present a contextual modeling on auxiliary points (CMAP) method for image reranking.With richer contextual activations, the Jaccard similarity coefficient defined by local distribution achieves more reliable outputs as well as more stable parameters. Extensive experiments demonstrate the robustness and effectiveness of the proposed method.