3D Block Matching Algorithm in Concealed Image Recognition and E-Commerce Customer Segmentation

E-commerce is the main means of commodity circulation in the future, and its development model needs to establish a product recommendation network for different customer groups. Under the unified mode of global market, accurately grasping the appearance requirements of customers' products is th...

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Bibliographic Details
Published in:IEEE sensors journal 2020-10, Vol.20 (20), p.11761-11769
Main Author: Liu, Fuxiang
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Commodities
Companies
concealed image
customer segmentation
Customers
e-commerce
Electronic commerce
Fourier transforms
Frequency-domain analysis
Global marketing
image recognition
Image segmentation
Matching
Object recognition
Three dimensional models
Three-dimensional displays
Three-dimensional matching
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Summary:E-commerce is the main means of commodity circulation in the future, and its development model needs to establish a product recommendation network for different customer groups. Under the unified mode of global market, accurately grasping the appearance requirements of customers' products is the prerequisite for the development of e-commerce enterprises. Based on the improved 3D block matching algorithm, this study identified and analyzed the internationalized commodity images of e-commerce networks, and combined customer actual operations to conduct customer clustering to accurately grasp customer demand information, realized e-commerce customer segmentation, and laid the foundation for personalized recommendation. In addition, this paper constructed an e-commerce customer segmentation model based on improved three-dimensional block matching algorithm and performed performance analysis on the improved algorithm and system model of this study through experimental verification. The research shows that the algorithm and model of this study have certain practical effects and can provide theoretical reference for subsequent related research.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2936169