Implementation and analysis of affine propagationalgorithm in image clustering applications
仿射传播算法在图像聚类应用中的实现与分析

Recently, the clustering methods based on partitioning are widely used in the field of data and image clustering. For the most widely k-means being slow and poor effect, this paper presented an improved algorithm based on affinity propagation, which was applied to image clustering. First, it presented the method combing color, shape and texture feature for efficient image retrieval. Then, on the basis of comprehensive characteristic model, it introduced a novel clustering method based on affinity propagation algorithm, Finally, it compared the results of affinity propagation with k-means in the MIT image database. The simulation experimental results show that the proposed method is superior to the traditional k-means clustering algorithm in the speed and effect of clustering. In addition, it is effective in exactness and real-time property.