Deep Learning Method for Fine-Grained Image Categorization
Fine-grained image categorization aims to distinguish the sub-categories from a certain category of images. Generally, fine-grained data sets have the characteristics of the intra-class similarity and inter-class variation, which makes the task of fine-grained image categorization more challenging....
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021-10-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2908.shtml |
| _version_ | 1819117512295448576 |
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| author | LI Xiangxia, JI Xiaohui, LI Bin |
| author_facet | LI Xiangxia, JI Xiaohui, LI Bin |
| author_sort | LI Xiangxia, JI Xiaohui, LI Bin |
| collection | DOAJ |
| description | Fine-grained image categorization aims to distinguish the sub-categories from a certain category of images. Generally, fine-grained data sets have the characteristics of the intra-class similarity and inter-class variation, which makes the task of fine-grained image categorization more challenging. With the increasing development of deep learning, the methods of fine-grained image categorization based on deep learning exhibit more powerful feature representation and generalization capabilities, and can obtain more accurate and stable classification results. Therefore, deep learning has been attracting more and more attentions and research from the researchers in the fine-grained image categorization. In this paper, starting from the background of fine-grained image categorization, the difficulties and the meaning of fine-grained image categorization are introduced. Then, from the perspectives of strong supervision and weak supervision, this paper reviews the research progress of fine-grained image classification algorithms based on deep learning, and a variety of typical classification algorithms with excellent performance are introduced. In addition, the YOLO (you only look once), multi-scale CNN (convolutional neural network), and GAN (generative adversarial networks) model are further discussed in the application of fine-grained image categorization, the perfor-mance of the latest relevant fine-grained data augmentation methods is compared and an analysis of different types of fine-grained categorization methods is made under complex scenarios. Finally, by comparing and summarizing the categorization algorithms, the future improvement directions and challenges are discussed. |
| first_indexed | 2024-12-22T05:34:09Z |
| format | Article |
| id | doaj.art-fabc238b9cc34129a1020da56d241550 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-22T05:34:09Z |
| publishDate | 2021-10-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-fabc238b9cc34129a1020da56d2415502022-12-21T18:37:23ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182021-10-0115101830184210.3778/j.issn.1673-9418.2103019Deep Learning Method for Fine-Grained Image CategorizationLI Xiangxia, JI Xiaohui, LI Bin01. School of Information, Guangdong University of Finance & Economics, Guangzhou 510320, China 2. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaFine-grained image categorization aims to distinguish the sub-categories from a certain category of images. Generally, fine-grained data sets have the characteristics of the intra-class similarity and inter-class variation, which makes the task of fine-grained image categorization more challenging. With the increasing development of deep learning, the methods of fine-grained image categorization based on deep learning exhibit more powerful feature representation and generalization capabilities, and can obtain more accurate and stable classification results. Therefore, deep learning has been attracting more and more attentions and research from the researchers in the fine-grained image categorization. In this paper, starting from the background of fine-grained image categorization, the difficulties and the meaning of fine-grained image categorization are introduced. Then, from the perspectives of strong supervision and weak supervision, this paper reviews the research progress of fine-grained image classification algorithms based on deep learning, and a variety of typical classification algorithms with excellent performance are introduced. In addition, the YOLO (you only look once), multi-scale CNN (convolutional neural network), and GAN (generative adversarial networks) model are further discussed in the application of fine-grained image categorization, the perfor-mance of the latest relevant fine-grained data augmentation methods is compared and an analysis of different types of fine-grained categorization methods is made under complex scenarios. Finally, by comparing and summarizing the categorization algorithms, the future improvement directions and challenges are discussed.http://fcst.ceaj.org/CN/abstract/abstract2908.shtmlfine-grained image categorizationdeep learningconvolutional neural network (cnn)feature extraction |
| spellingShingle | LI Xiangxia, JI Xiaohui, LI Bin Deep Learning Method for Fine-Grained Image Categorization Jisuanji kexue yu tansuo fine-grained image categorization deep learning convolutional neural network (cnn) feature extraction |
| title | Deep Learning Method for Fine-Grained Image Categorization |
| title_full | Deep Learning Method for Fine-Grained Image Categorization |
| title_fullStr | Deep Learning Method for Fine-Grained Image Categorization |
| title_full_unstemmed | Deep Learning Method for Fine-Grained Image Categorization |
| title_short | Deep Learning Method for Fine-Grained Image Categorization |
| title_sort | deep learning method for fine grained image categorization |
| topic | fine-grained image categorization deep learning convolutional neural network (cnn) feature extraction |
| url | http://fcst.ceaj.org/CN/abstract/abstract2908.shtml |
| work_keys_str_mv | AT lixiangxiajixiaohuilibin deeplearningmethodforfinegrainedimagecategorization |