Image feature matching is a key link for the implementation of content-based image retrieval (CBIR), which mainly relies on the similarity measure between the features of two images. To improve the retrieval performance of CBIR, this paper proposes an effective similarity measure method—similarity measure based on k-nearest neighbors of images (SBkNN). In the proposed SBkNN method , the similarity between query image and retrieved image is obtained by calculating the probability for the two images belonging to the same semantic category (no matter what kind of semantic category), and the probability can be obtained by analyzing the distance between the two images and their k-nearest neighbors， respectively. Finally, the comparison between the proposed SBkNN method and traditional similarity measure is implemented on Corel5K dataset. Experimental results show that the proposed SBkNN method significantly improves the retrieval performance of CBIR.