A Gallery-Guided Graph Architecture for Sequential Impurity Detection

doi:10.1109/ACCESS.2019.2946861

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	A Gallery-Guided Graph Architecture for Sequential Impurity Detection
	He, Wenhao 1; Song, Haitao 1; Guo, Yue 1; Yin, Xiaoyi 2; Wang, Xiaonan 1; Bian, Guibin 1; Qian, Wen 1
刊名	IEEE ACCESS
	2019
卷号	7 页码:149105-149116
关键词	Impurities Proposals Feature extraction Training Convolutional neural networks Task analysis Impurity detection gallery-guided graph feature embedding graph convolutional neural network
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2946861
英文摘要	Ambiguous appearance discrimination plays an important role in the impurity detection task. Among the majority of deep learning models, images from every sequence are processed separately instead of being considered collectively. Therefore, the outputs of these models given a single region proposal might not be accurate. In this paper, a gallery-guided graph architecture is proposed and integrated to overcome such limitations. Specifically, region proposals are firstly generated using a two-stream fusion network; then their feature embeddings are extracted from a convolutional neural network by reducing intra-class variations while increasing inter-class ones. Secondly, a graph representing clusters among different training sequences updates relationships between region proposals in the test sequence. Finally, the features of the graph are classified by a graph convolutional neural network. Different from those learned weights in conventional common object detectors, region features from all the training sequences are explicitly integrated into a gallery-guided graph architecture. Extensive experiments on IML-DET dataset demonstrate that our proposed method can obtain competitive performances compared with previous state-of-the-art object detection approaches transferred into this task.
资助项目	National Key Research and Development Program of China[2018YFB1306300] ; National Natural Science Foundation (NNSF) of China[61421004]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000497163000003
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/14948]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Guo, Yue
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	He, Wenhao,Song, Haitao,Guo, Yue,et al. A Gallery-Guided Graph Architecture for Sequential Impurity Detection[J]. IEEE ACCESS,2019,7:149105-149116.
APA	He, Wenhao.,Song, Haitao.,Guo, Yue.,Yin, Xiaoyi.,Wang, Xiaonan.,...&Qian, Wen.(2019).A Gallery-Guided Graph Architecture for Sequential Impurity Detection.IEEE ACCESS,7,149105-149116.
MLA	He, Wenhao,et al."A Gallery-Guided Graph Architecture for Sequential Impurity Detection".IEEE ACCESS 7(2019):149105-149116.