基于多特征融合的商品识图匹配算法研究

Matching algorithm of product image based on multi-feature fusion

随着近几年无人超市的不断发展成熟,自助购物越来越普及。如果商品售出后没能及时补充,会影响消费者的购买意愿。为提升商品图像识别的准确率,采用多特征融合的方法,即将多种算法的特征联合,形成优势互补。采用融合SIFT特征和灰度共生矩阵特征的方法完成货架商品图像的匹配。实验表明,该方法对比灰度共生矩阵方法准确率提升20.6%,对比SIFT算法和PCA-SIFT算法准确率分别提升8.9%和5.6%;处理时间对比以上三种算法略有增加。此方法还可用于分析货物受欢迎程度以及确认哪些柜台需要加货等,从而及时有效地对短缺的商品进行补充。

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.