Hybrid incremental learning of new data and new classes for hand-held object recognition
文献类型：期刊论文
| 作者 | Min WQ(闵巍庆)2,3; Li, Xue2; Jiang SQ(蒋树强)2,4; Chen, Chengpeng1,4 |
| 刊名 | Journal of Visual Communication and Image Representation
 |
| 出版日期 | 2019 |
| 卷号 | 58页码:138-148 |
| 关键词 | Incremental learning Object recognition SVM Human-machine interaction |
| ISSN号 | 1047-3203 |
| 产权排序 | 2 |
| 英文摘要 | Intelligence 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. |
| WOS关键词 | CLASSIFICATION ; MECHANISMS ; FEATURES ; ONLINE |
| 资助项目 | Beijing 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研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000457668100015 |
| 资助机构 | Beijing 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 |
| 源URL | [http://ir.sia.cn/handle/173321/23670]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Jiang SQ(蒋树强) |
| 作者单位 | 1.State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2.Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 3.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Min WQ,Li, Xue,Jiang SQ,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 | Min WQ,Li, Xue,Jiang SQ,&Chen, Chengpeng.(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 | Min WQ,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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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