Land mobile distance-based image spam similarity-detection method

Abstract

The invention discloses a land mobile distance-based image spam similarity-detection method. Invariant area features of a spam are extracted from a picture by utilizing a scale invariant feature transform algorithm, and similarity between the picture to be detected and the picture in a spam feature library is calculated by using a land mobile distance, thereby detecting an image spam. In the land mobile distance-based image spam similarity-detection method provided by the invention, the local invariant features of the picture are utilized. In the prior art of detecting the spam by utilizing the similarity, a Euclidean distance is mainly utilized, cannot process features with variant structure sizes, and is required to cluster and normalize the features first, so the detection speed is influence. The local invariant features with the variant structure sizes are directly processed by utilizing the land mobile distance, so the method greatly increases the detection speed of the image spam and simultaneously ensures high accuracy and low false rate.

Description

technical field [0001] The present invention is a method for extracting local invariant features of pictures and using a similarity measurement method of land movement distance to realize the detection of image-type spam, which mainly solves the detection efficiency and recall rate of image-type spam in today's technology Low-level problems, belonging 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 Recognition) technology to detect the text information of image spam. Compare...

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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