Knowledge Commons of Institute of Automation,CAS
Few-Shot Visual Classification Using Image Pairs With Binary Transformation | |
Zhang, Chunjie1,2,3; Li, Chenghua3; Cheng, Jian4,5 | |
发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY |
ISSN | 1051-8215 |
2020-09-01 | |
卷号 | 30期号:9页码:2867-2871 |
通讯作者 | Zhang, Chunjie(ivazhangchunjie@gmail.com) |
摘要 | Accurately classifying images using few-shot samples have been widely explored by researchers. However, these methods have two drawbacks. First, images are often used independently. Second, class imbalance is ignored and hinders the classification accuracy with the increment of classes. To tackle these two drawbacks, in this paper, we propose a novel visual classification method using image pairs with binary transformation (IPBT). For one image, we bundle it with each training image into an image pair by concatenating the representations of the two images along with their similarity. The class consistency of two images is used to split the image pairs into binary groups. One group contains image pairs of the same class, while the other group consists of images pairs belonging to different classes. We train classifiers to separate the binary groups apart. To classify a testing image, we first bundle it with all the training images that are then predicted using the learned binary classifier. The image pair with the largest response is selected, and the testing image is assigned to the same class of the paired image. We conduct few-shot visual classification experiments on three public image datasets. The experimental results and analysis show the effectiveness of the proposed IPBT method. |
关键词 | Training Visualization Testing Correlation Image representation Automation Convolutional neural networks Few-shot classification visual classification image pair binary transformation object categorization |
DOI | 10.1109/TCSVT.2019.2920783 |
关键词[WOS] | LOW-RANK ; LABEL PROPAGATION ; FRAMEWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Science Foundation of China (NSFC)[61872362] ; State Grid Corporation Science and Technology Project[5200-201916261A-0-0-00] |
项目资助者 | National Science Foundation of China (NSFC) ; State Grid Corporation Science and Technology Project |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000567499300007 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41941 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Zhang, Chunjie |
作者单位 | 1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 2.Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang, Chunjie,Li, Chenghua,Cheng, Jian. Few-Shot Visual Classification Using Image Pairs With Binary Transformation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2020,30(9):2867-2871. |
APA | Zhang, Chunjie,Li, Chenghua,&Cheng, Jian.(2020).Few-Shot Visual Classification Using Image Pairs With Binary Transformation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,30(9),2867-2871. |
MLA | Zhang, Chunjie,et al."Few-Shot Visual Classification Using Image Pairs With Binary Transformation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 30.9(2020):2867-2871. |
条目包含的文件 | 条目无相关文件。 |
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。
修改评论