AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization
文献类型：期刊论文
| 作者 | Yao, Hantao2,3; Zhang, Shiliang4; Yan, Chenggang5; Zhang, Yongdong2,3; Li, Jintao2; Tian, Qi1 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2018 |
| 卷号 | 27期号:1页码:10-23 |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2017.2751960 |
| 英文摘要 | Compared with traditional image classification, fine-grained visual categorization is a more challenging task, because it targets to classify objects belonging to the same species, e.g., classify hundreds of birds or cars. In the past several years, researchers have made many achievements on this topic. However, most of them are heavily dependent on the artificial annotations, e.g., bounding boxes, part annotations, and so on. The requirement of artificial annotations largely hinders the scalability and application. Motivated to release such dependence, this paper proposes a robust and discriminative visual description named Automated Bi-level Description (AutoBD). "Bi-level" denotes two complementary part-level and object-level visual descriptions, respectively. AutoBD is "automated," because it only requires the image-level labels of training images and does not need any annotations for testing images. Compared with the part annotations labeled by the human, the image-level labels can be easily acquired, which thus makes AutoBD suitable for large-scale visual categorization. Specifically, the part-level description is extracted by identifying the local region saliently representing the visual distinctiveness. The object-level description is extracted from object bounding boxes generated with a co-localization algorithm. Although only using the image-level labels, AutoBD outperforms the recent studies on two public benchmark, i.e., classification accuracy achieves 81.6% on CUB-200-2011 and 88.9% on Car-196, respectively. On the large-scale Birdsnap data set, AutoBD achieves the accuracy of 68%, which is currently the best performance to the best of our knowledge. |
| 资助项目 | National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[61572050] ; National Nature Science Foundation of China[91538111] ; National Nature Science Foundation of China[61429201] ; National Nature Science Foundation of China[61428207] ; Beijing Advanced Innovation Center for Imaging Technology[BAICIT-2016009] ; ARO[W911NF-15-1-0290] ; NEC Laboratories of America ; Blippar |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000413256300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/6877]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Yongdong; Tian, Qi |
| 作者单位 | 1.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China 5.Hangzhou Dianzi Univ, Sch Inst Informat & Control, Hangzhou 541004, Zhejiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Hantao,Zhang, Shiliang,Yan, Chenggang,et al. AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(1):10-23. |
| APA | Yao, Hantao,Zhang, Shiliang,Yan, Chenggang,Zhang, Yongdong,Li, Jintao,&Tian, Qi.(2018).AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(1),10-23. |
| MLA | Yao, Hantao,et al."AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.1(2018):10-23. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。