Abstract: With the surge in the number of e-commerce users, various problems continue to emerge. Among them, the incidents of plagiarism and copying information from other stores in the same industry often occur, but plagiarized image information is more difficult to detect similarity than text information, because plagiarists often may crop, rotate or add filter to image information, in addition, they can process images with PS and other technologies, which makes it difficult to detect the similarity between the processed image and the original image. However, the manual comparison is inefficient and costly, so a system based on an algorithm that can quickly calculate the similarity of commodity images is required to solve this problem. Scale-invariant feature transform (SIFT) descriptors can solve the limitations of traditional algorithms for low similarity of rotated images. The accuracy of the algorithm SIFT is high, and it can describe rich feature information. Based on the introduction of the traditional image Hash algorithm, a similar nearest neighbor matching algorithm based on SIFT descriptor is proposed for the similarity comparison of electric drill product images. The original image is cropped, added filter, added contrast, rotated, added watermarks etc, respectively, and these processed images are all compared with the original images about similarity. The experimental results show that the similar nearest neighbor matching algorithm has better accuracy than Hash algorithm and SIFT algorithm, and it can identify plagiarized image information more accurately.

Key words: image similarity, image Hash algorithm, SIFT algorithm, nearest neighbor matching