仿射传播算法在图像聚类应用中的实现与分析

Implementation and analysis of affine propagation algorithm in image clustering applications

近年来,基于划分的聚类算法被广泛应用于数据和图像聚类中。针对应用最为广泛的k-均值算法在图像聚类中存在的聚类速度慢、效果差等问题,提出一种仿射传播算法应用于图像聚类中。提取图像中颜色、形状和纹理等特征向量,利用仿射传播算法对综合特征向量模型进行聚类,最后将仿射传播算法和k-均值算法对MIT图像的聚类作了对比分析。仿真实验表明,仿射传播算法在速度和聚类效果上均优于已有的k-均值算法,在准确性和实时性方面均能达到较好的效果。

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.