FewHshot image classification method based on sliding feature vectors

doi:10.13229/j.cnki.jdxbgxb20200532

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	FewHshot image classification method based on sliding feature vectors
	Cao, Jie 1,2; Qu, Xue 3; Li, Xiao-Xu 2
刊名	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
	2021-09-01
卷号	51 期号:5 页码:1785-1791
关键词	Query processing Vector spaces Class distributions Classification methods Computer applications technologies Feature space Features vector Few-shot learning Images classification Local feature Metric learning Misclassifications
ISSN号	16715497
DOI	10.13229/j.cnki.jdxbgxb20200532
英文摘要	In the task of few-shot image classification, the extremely limited number of labeled examples per class can hardly represent the real class distribution effectively, which is the main reason for misclassification. To tackle this problem, we propose a method which named Sliding Feature Vectors Neural Network (SFV). The method aims to assemble all the local sliding feature vectors of samples from the same class to construct the class-level feature spaces, and then it utilized the image-to-class measure to classify the query samples. That means on the measure stage, SFV compare the similarity between the class and the query sample. SFV expands the class feature space by adding the edge information of feature blocks and correlation of their position and structures to maximize the utilization of the deep feature maps when the sample is extremely limited, which can ease overfitting problem caused by small sample data. Experimental study on benchmark datasets consistently shows its superiority over the related other framework, especially on fine-grained datasets, it achieves state-of-the-art. © 2021, Jilin University Press. All right reserved.
语种	中文
出版者	Editorial Board of Jilin University
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/150886]
专题	兰州理工大学
作者单位	1.Engineering Research Center of Urban Railway Transportation of Gansu Province, Lanzhou; 730050, China; 2.School of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 3.School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Cao, Jie,Qu, Xue,Li, Xiao-Xu. FewHshot image classification method based on sliding feature vectors[J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),2021,51(5):1785-1791.
APA	Cao, Jie,Qu, Xue,&Li, Xiao-Xu.(2021).FewHshot image classification method based on sliding feature vectors.Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),51(5),1785-1791.
MLA	Cao, Jie,et al."FewHshot image classification method based on sliding feature vectors".Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 51.5(2021):1785-1791.