CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Learning Deep Relationship for Object Detection
Xu, Nuo; Huo, Chunlei; Huo CL(霍春雷)
Source PublicationIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
2018
VolumeE101DIssue:1Pages:273-276
SubtypeArticle
AbstractObject detection has been a hot topic of image processing, computer vision and pattern recognition. In recent years, training a model from labeled images using machine learning technique becomes popular. However, the relationship between training samples is usually ignored by existing approaches. To address this problem, a novel approach is proposed, which trains Siamese convolutional neural network on feature pairs and finely tunes the network driven by a small amount of training samples. Since the proposed method considers not only the discriminative information between objects and background, but also the relationship between intraclass features, it outperforms the state-of-arts on real images.
KeywordObject Detection Siamese Convolutional Neural Network Remote Sensing Images Relationship Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1587/transinf.2017EDL8131
WOS KeywordSUPPORT VECTOR MACHINES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91438105 ; 61375024)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000431760600036
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15383
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorHuo CL(霍春雷)
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Xu, Nuo,Huo, Chunlei,Huo CL. Learning Deep Relationship for Object Detection[J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS,2018,E101D(1):273-276.
APA Xu, Nuo,Huo, Chunlei,&霍春雷.(2018).Learning Deep Relationship for Object Detection.IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS,E101D(1),273-276.
MLA Xu, Nuo,et al."Learning Deep Relationship for Object Detection".IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E101D.1(2018):273-276.
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