Method for detecting image-based spam email by utilizing improved gauss hybrid model classifier

Abstract

The invention discloses a method for detecting a spam email by utilizing an improved gauss hybrid model classifier, comprising the following steps of: extracting invariant region features of spam information in a picture by utilizing an accelerative extract algorithm of a robust feature, executing the fitting of a gauss hybrid model on the invariant region features, and executing the evaluation of weight, mean and covariance matrixes by using an expectation maximization method, wherein the method specifically comprises the following steps of: labeling pictures of a data set to be detected, and dividing the pictures into spam pictures and regular pictures; extracting vectors of local invariant features of all data sets by utilizing the accelerative extract algorithm of the robust feature; executing density function fitting on the local invariant features by utilizing the gauss hybrid model to obtain mean and covariance matrixes of the all pictures; improving a mean clustering algorithm to make the mean clustering algorithm be suitable for clustering special feature vectors obtained in the previous step, taking cross entropy as an measurement index of the similarity of gauss hybrid distributions, and realizing the mean clustering algorithm based on the gauss hybrid model; and establishing a classifier by utilizing the mean clustering algorithm based on the gauss hybrid model.

Description

technical field [0001] The present invention uses the Gaussian mixture model in statistical thinking to perform density function fitting on the local invariant features of pictures. A mean clustering algorithm and a classification model based on Gaussian mixture model are proposed to detect image spam. It mainly solves the problems of low detection efficiency and recall rate of image spam in today's technology, and belongs to the field of data mining and machine learning. Background technique [0002] E-mail has become an important way for people to communicate on the Internet, but due to the huge commercial, economic and political interests, the amount of spam has expanded rapidly. The image-based spam that was prevalent at the beginning was to embed spam information such as advertisements into the image in the form of text. Hrishikesh et al. are using the mined text and color features to classify the mail [1]. In 2006, Fumera et al. proposed an OCR (Optical Character Rec...

AI Technical Summary

Problems solved by technology

Distinguish junk pictures by calculating the threshold value. Although this method uses statistical knowledge to calculate more accurately, the amount of calculation is too large, and the time complexity of the algorithm is high, which is not conducive to practical applications.

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