Abstract
The 21st century is the era of big data in the Internet. Online shopping has become a trend, and e-commerce has developed rapidly. With the exponential increase of the amount of commodity image data, the management of massive commodity image database restricts the development of e-commerce to some extent. In order to effectively manage goods and improve the accuracy and efficiency of product image retrieval, this paper uses content-based methods to classify e-commerce images. Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm-AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. By simulating the commodity identification dataset RPC, the results show that the proposed method has obvious advantages in the classification training time and classification accuracy of e-commerce images.
Highlights
Big data is one of the mainstream topics in the current information age [1]
In Internet e-commerce, the user is faced with the product itself, but the product image information and some simple annotations, such as the name, origin, size, price and other basic information of the product, so the image becomes the main information for the transmission of commodity information carrier
In order to solve the problems of insufficient classification accuracy and long training time in e-commerce image classification, this paper proposes a content-based e-commerce image classification method
Summary
Big data is one of the mainstream topics in the current information age [1]. Our living environment is full of vast amounts of information, and people live in the ocean of information. Among the kinds of information that people receive, the most intuitive and most important thing is the image information received through the vision [2]. More than 70% of the human receiving information is received by the visual [3]. In the field of e-commerce, automatic classification of product images can provide fast transaction query for both parties, determine the placement strategy of products and intelligent recommendation of products of interest to users, effectively improving the overall efficiency of the e-commerce market. This is an urgent requirement for e-commerce intelligence. The research on e-commerce image classification has very important practical significance
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