中图分类号： TP391
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.04.011
引用格式： 王鑫城，范红，刘锡泽，等. 基于多特征融合的商品识图匹配算法研究[J].信息技术与网络安全，2021，
40(4)：70-74.

Matching algorithm of product image based on multi-feature fusion

Abstract： With the continuous development of unmanned supermarkets in recent years, self-service shopping has become more and more popular. If the goods are not replenished in time after they are sold, it will affect consumers′ willingness to buy. In order to improve the accuracy of product image recognition, a multi-feature fusion method is adopted, that is, the features of multiple algorithms are combined to form complementary advantages. The method of fusing SIFT features and gray level co-occurrence matrix features is used in this paper to complete the matching of shelf product images. Experiments show that the accuracy of this method is increased by 20.6% compared with the gray-level co-occurrence matrix method, and the accuracy of the SIFT algorithm and the PCA-SIFT algorithm are increased by 8.9% and 5.6% respectively; the processing time is slightly increased compared with the above three algorithms. This method can also be used to analyze the popularity of goods and confirm which counters need to be restocked, so as to supplement the shortage of goods in a timely and effective manner.

Key words : image matching；SIFT features；gray level co-occurrence matrix；feature fusion