关键词: 频繁集, 特征提取, 遥感图像, 期望最大（EM）聚类

Abstract: In the image analysis field，there have been some studies on association rules mining for texture feature from the affair data set that generates from the image neighborhood pixels，but association rules just keeping the frequent itemsets having maximized items leads the lose of information of the image.Extracting the feature from image based on frequent itemsets is proposed，this method first selects out the candidate frequent itemsets founded on the degree of frequency and the locality distributing，then constructs the feature set by defining the value of spatial control for every frequent itemset.The simulation is done with remote sensor image，because the EM clustering algorithm is dominated by its initializing，the experimentation specifies the different number of clusters for every feature set and determines the final clustering results by comparing the log likelihood.Experiment results show that frequent feature set can provide satisfactory expression for image.

Key words: frequent itemset, feature extraction, remote sensor image, Expectation Maximization（EM） clustering