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Many image retrieval users are concerned about the diversity of the retrieval results, as well as their relevance. In this paper, we develop a post-processing system, which is based on affinity propagation clustering on manifolds, to improve the diversity of the retrieval results without reduction of their relevance. In order to obtain the top 20 outputs (usually only the top 20 outputs of the retrieval results are shown to users) containing diverse items representing different sub-topics, a modified affinity propagation clustering on manifolds, whose parameters are optimized by minimizing the Davies-Bouldin criterion, is proposed and then performed on the top hundreds of output images of the previous support vector machine (SVM) system. Finally, after getting the clusters, to diversify the top retrieval results, we put the image with the lowest rank in each cluster into the top of the answer list. We test our proposed system on the ImageCLEF PHOTO 2008 task. The experimental results show that our method performs better in enhancing the diversity performance of the image retrieval results, comparing to other diversifying methods such as K-means, Quality Threshold (QT), date clustering (ClusterDMY), and so on. Furthermore, our method does not lead to any loss of the relevance of the retrieval results.