Retracted: Content-Based E-Commerce Image Classification Research
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 e...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9174782/ |
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| _version_ | 1841557052102541312 |
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| author | Xiaoli Zhang |
| author_facet | Xiaoli Zhang |
| author_sort | Xiaoli Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d77c458b290b4a989c2670c185727913 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d77c458b290b4a989c2670c1857279132025-01-07T00:00:53ZengIEEEIEEE Access2169-35362020-01-01816021316022010.1109/ACCESS.2020.30188779174782Retracted: Content-Based E-Commerce Image Classification ResearchXiaoli Zhang0https://orcid.org/0000-0002-3673-2188School of Business, Xuchang University, Xuchang, ChinaThe 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.https://ieeexplore.ieee.org/document/9174782/ |
| spellingShingle | Xiaoli Zhang Retracted: Content-Based E-Commerce Image Classification Research IEEE Access |
| title | Retracted: Content-Based E-Commerce Image Classification Research |
| title_full | Retracted: Content-Based E-Commerce Image Classification Research |
| title_fullStr | Retracted: Content-Based E-Commerce Image Classification Research |
| title_full_unstemmed | Retracted: Content-Based E-Commerce Image Classification Research |
| title_short | Retracted: Content-Based E-Commerce Image Classification Research |
| title_sort | retracted content based e commerce image classification research |
| url | https://ieeexplore.ieee.org/document/9174782/ |
| work_keys_str_mv | AT xiaolizhang retractedcontentbasedecommerceimageclassificationresearch |