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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| Veröffentlicht in: | IEEE sensors journal 2020-10, Vol.20 (20), p.11761-11769 |
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| creator | Liu, Fuxiang |
| description | 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. |
| doi_str_mv | 10.1109/JSEN.2019.2936169 |
| format | Article |
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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.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2019.2936169</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>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</subject><ispartof>IEEE sensors journal, 2020-10, Vol.20 (20), p.11761-11769</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The research shows that the algorithm and model of this study have certain practical effects and can provide theoretical reference for subsequent related research.</description><subject>Algorithms</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Commodities</subject><subject>Companies</subject><subject>concealed image</subject><subject>customer segmentation</subject><subject>Customers</subject><subject>e-commerce</subject><subject>Electronic commerce</subject><subject>Fourier transforms</subject><subject>Frequency-domain analysis</subject><subject>Global marketing</subject><subject>image recognition</subject><subject>Image segmentation</subject><subject>Matching</subject><subject>Object recognition</subject><subject>Three dimensional models</subject><subject>Three-dimensional displays</subject><subject>Three-dimensional matching</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kL1OwzAURi0EEqXwAIjFEnOKHTtxPJZQoKiAREFisxznJk1JbLDTgbcnUSume4fz3Z-D0CUlM0qJvHlaL15mMaFyFkuW0lQeoQlNkiyigmfHY89IxJn4PEVnIWzJQIpETJBid_i2deYLP-vebBpb43lbO9_0mw43FufOGtAtlHjZ6RrwGxhX26ZvnMXalngR5a7rwBvA-S70bmjxGuoObK9H6BydVLoNcHGoU_Rxv3jPH6PV68Myn68iM9zbRwUlhaE80bEohy8yqWNaVTwGWRpNSFYxwYBJWZCsFAmIghdcQMoLXVGjC82m6Ho_99u7nx2EXm3dztthpYo55ynjgoqBonvKeBeCh0p9-6bT_ldRokaPavSoRo_q4HHIXO0zDQD881lGEsYZ-wPp3W63</recordid><startdate>20201015</startdate><enddate>20201015</enddate><creator>Liu, Fuxiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| 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 |
| title | 3D Block Matching Algorithm in Concealed Image Recognition and E-Commerce Customer Segmentation |
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