Effective Use Of Frequent Histogram Patterns For Image Classification

This paper introduces the process of mining based on frequent histogram patterns and also evaluation the method. We accomplished the experiment Of mining process completely, then put forward a kind of image spam filtering method based on frequent histogram patterns and designed a system.The main work of system design is divided into two parts. One Section of our mean work is the process of mining frequent histogram patterns:Firstly,We extract SIFT feature from test images.Due to there are some interferential points among the results,we denoise these points by Grabcut,as a result,Characteristics of SIFT feature were obtained from all the interesting parts of the test set;Secondly,Image bag of word model, which is established on the K-means local word bag model, is expressed in the form of histogram patterns.Local word bag model reflects more details information of the picture than the global model, so it will be more accuracy to characterized the information feature points.Thirdly, to get the most frequent emerged histogram set of a picture, the improved Apriori algorithm was frequently used to mine itemsets from picture histogram model. Then the closed itemsets mining technique was used to extract closed frequent item-sets from frequent histogram sets. Finally, to select the most representative and discrimination model, two evaluation function models were used to evaluate all closed frequent histogram sets. Eventually, the obtained frequent closure histogram set model was used to represent the image feature.In the second section, the closed frequent histogram set patterns of the mined feature image is used in the practical applications, and a image filtering system based on frequent histogram patterns is designed through the previous work. At last, the process of the system is introduced, and the filtering precision is given by experiment.