IMR OpenIR
Hybrid incremental learning of new data and new classes for hand-held object recognition
Chen, Chengpeng1,4; Min, Weiqing2,3; Li, Xue2; Jiang, Shuqiang2,4
Corresponding AuthorJiang, Shuqiang(sqjiang@ict.ac.cn)
2019
Source PublicationJOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
ISSN1047-3203
Volume58Pages:138-148
AbstractIntelligence technology is an important research area. As a very special yet important case of object recognition, hand-held object recognition plays an important role in intelligence technology for its many applications such as visual question-answering and reasoning. In real-world scenarios, the datasets are open-ended and dynamic: new object samples and new object classes increase continuously. This requires the intelligence technology to enable hybrid incremental learning, which supports both data-incremental and class-incremental learning to efficiently learn the new information. However, existing work mainly focuses on one side of incremental learning, either data-incremental or class-incremental learning while do not handle two sides of incremental learning in a unified framework. To solve the problem, we present a Hybrid Incremental Learning (HIL) method based on Support Vector Machine (SVM), which can incrementally improve its recognition ability by learning new object samples and new object concepts during the interaction with humans. In order to integrate data-incremental and class-incremental learning into one unified framework, HIL adds the new classification-planes and adjusts existing classification-planes under the setting of SVM. As a result, our system can simultaneously improve the recognition quality of known concepts by minimizing the prediction error and transfer the previous model to recognize unknown objects. We apply the proposed method into hand-held object recognition and the experimental results demonstrated its advantage of HIL. In addition, we conducted extensive experiments on the subset of ImageNet and the experimental results further validated the effectiveness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.
KeywordIncremental learning Object recognition SVM Human-machine interaction
Funding OrganizationBeijing Natural Science Foundation ; National Natural Science Foundation of China ; Lenovo Outstanding Young Scientists Program ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; China Postdoctoral Science Foundation ; State Key Laboratory of Robotics
DOI10.1016/j.jvcir.2018.11.009
Indexed BySCI
Language英语
Funding ProjectBeijing Natural Science Foundation[4174106] ; National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61602437] ; Lenovo Outstanding Young Scientists Program ; National Program for Special Support of Eminent Professionals ; National Program for Support of Top-notch Young Professionals ; China Postdoctoral Science Foundation[2017T100110] ; State Key Laboratory of Robotics
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000457668100015
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/131750
Collection中国科学院金属研究所
Corresponding AuthorJiang, Shuqiang
Affiliation1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
3.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Liaoning, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Chen, Chengpeng,Min, Weiqing,Li, Xue,et al. Hybrid incremental learning of new data and new classes for hand-held object recognition[J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,2019,58:138-148.
APA Chen, Chengpeng,Min, Weiqing,Li, Xue,&Jiang, Shuqiang.(2019).Hybrid incremental learning of new data and new classes for hand-held object recognition.JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,58,138-148.
MLA Chen, Chengpeng,et al."Hybrid incremental learning of new data and new classes for hand-held object recognition".JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 58(2019):138-148.
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