Content-based image retrieval systems typically rely on a similarity measure between image vector representations, such as in bag-of-words, to rank the database images in decreasing order of expected relevance to the query. However, the inherent asymmetry of k-nearest neighborhoods is not properly accounted for by traditional similarity measures, possibly leading to a loss of retrieval accuracy. This paper addresses this issue by proposing similarity measures that use neighborhood information to assess the relationship between images. First, we extend previous work on k-reciprocal nearest neighbors to produce new measures that improve over the original primary metric. Second, we propose measures defined on sets of shared nearest neighbors for re-ranking the shortlist. Both these methods are simple, yet they significantly improve the accuracy of image search engines on standard benchmark datasets.