中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
A Gallery-Guided Graph Architecture for Sequential Impurity Detection

文献类型：期刊论文

作者He, Wenhao1; Song, Haitao1; Guo, Yue1; Yin, Xiaoyi2; Wang, Xiaonan1; Bian, Guibin1; Qian, Wen1
刊名IEEE ACCESS
出版日期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
DOI10.1109/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
WOS记录号WOS:000497163000003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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.

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。