Picture similarity detecting and system

Abstract

The invention relates to a picture similarity detecting method and system. The detecting method comprises a step of screening out Internet picture feature points in an Internet picture and reference picture feature points in a reference picture, and a step of obtaining matched feature points in the Internet picture and the reference picture according to all Internet picture feature points and allreference picture feature points, wherein the Internet picture and the reference picture are similar to each other when the number of matched feature points is larger than or equal to a preset threshold. According to the embodiment of the present invention, by obtaining and comparing the feature points of two pictures, whether the feature points of the Internet picture and the reference picture are matched is judged, the number of matched feature points is counted, when the number of matched feature points is larger than or equal to the preset threshold, a condition that the Internet picture and the reference picture are similar to each other is confirmed, in the embodiment of the present invention, the amount of calculated data is small, the anti-noise capability is strong, and whether pictures are similar to each other is quickly identified and checked.

Description

technical field [0001] The invention relates to the technical field of picture recognition, in particular to a picture similarity detection method and system. Background technique [0002] At present, Google's image search function uses a perceptual hashing algorithm to detect similar images. The principle is to generate a fingerprint string for each image and compare the distance between the fingerprints of different images. The shorter the distance, the more similar the image. The implementation of this algorithm is relatively simple and fast, but when a small content change is made to the picture, such as adding a watermark or text to the picture, it cannot be recognized, and there is a problem that the recognition of similar pictures is not stable enough. [0003] In actual work, in order to improve the accuracy of image similarity detection, a method based on feature point matching is often used to achieve fast image matching. The method of feature point matching has hi...

AI Technical Summary

Problems solved by technology

The more popular feature point detection algorithms mainly include the Harris algorithm, as well as the SIFT algorithm with a faster detection speed. The Harris algorithm has poor anti-noise ability, and the SIFT algorithm has a high dimension of feature vectors, which requires a large amount of calculation data, takes a long time, and cannot be realized real-time effect

[0004] Therefore, the existing image similarity calculation method has the problems of low execution efficiency and unstable recognition

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