Commodity Image Classification Based on Improved Bag-of-Visual-Words Model
With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5556899 |
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| _version_ | 1850159762842320896 |
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| author | Huadong Sun Xu Zhang Xiaowei Han Xuesong Jin Zhijie Zhao |
| author_facet | Huadong Sun Xu Zhang Xiaowei Han Xuesong Jin Zhijie Zhao |
| author_sort | Huadong Sun |
| collection | DOAJ |
| description | With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively. |
| format | Article |
| id | doaj-art-b2b8a57ca4ee4b21bd09a440d7d0163f |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b2b8a57ca4ee4b21bd09a440d7d0163f2025-08-20T02:23:24ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55568995556899Commodity Image Classification Based on Improved Bag-of-Visual-Words ModelHuadong Sun0Xu Zhang1Xiaowei Han2Xuesong Jin3Zhijie Zhao4School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaWith the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively.http://dx.doi.org/10.1155/2021/5556899 |
| spellingShingle | Huadong Sun Xu Zhang Xiaowei Han Xuesong Jin Zhijie Zhao Commodity Image Classification Based on Improved Bag-of-Visual-Words Model Complexity |
| title | Commodity Image Classification Based on Improved Bag-of-Visual-Words Model |
| title_full | Commodity Image Classification Based on Improved Bag-of-Visual-Words Model |
| title_fullStr | Commodity Image Classification Based on Improved Bag-of-Visual-Words Model |
| title_full_unstemmed | Commodity Image Classification Based on Improved Bag-of-Visual-Words Model |
| title_short | Commodity Image Classification Based on Improved Bag-of-Visual-Words Model |
| title_sort | commodity image classification based on improved bag of visual words model |
| url | http://dx.doi.org/10.1155/2021/5556899 |
| work_keys_str_mv | AT huadongsun commodityimageclassificationbasedonimprovedbagofvisualwordsmodel AT xuzhang commodityimageclassificationbasedonimprovedbagofvisualwordsmodel AT xiaoweihan commodityimageclassificationbasedonimprovedbagofvisualwordsmodel AT xuesongjin commodityimageclassificationbasedonimprovedbagofvisualwordsmodel AT zhijiezhao commodityimageclassificationbasedonimprovedbagofvisualwordsmodel |