Discriminatively boosted image clustering with fully convolutional auto-encoders
文献类型：期刊论文
| 作者 | Li, Fengfu1,3; Qiao, Hong4,5,6; Zhang, Bo2,3
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2018-11-01 |
| 卷号 | 83页码:161-173 |
| 关键词 | Image clustering Fully convolutional auto-encoder Representation learning Discriminatively boosted clustering |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2018.05.019 |
| 英文摘要 | Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft k-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance. (C) 2018 Elsevier Ltd. All rights reserved. |
| 资助项目 | NNSF of China[91648205] ; NNSF of China[61627808] ; NNSF of China[61602483] ; NNSF of China[61603389] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000442172200012 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/31092]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang, Bo |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Fengfu,Qiao, Hong,Zhang, Bo. Discriminatively boosted image clustering with fully convolutional auto-encoders[J]. PATTERN RECOGNITION,2018,83:161-173. |
| APA | Li, Fengfu,Qiao, Hong,&Zhang, Bo.(2018).Discriminatively boosted image clustering with fully convolutional auto-encoders.PATTERN RECOGNITION,83,161-173. |
| MLA | Li, Fengfu,et al."Discriminatively boosted image clustering with fully convolutional auto-encoders".PATTERN RECOGNITION 83(2018):161-173. |
入库方式： OAI收割
来源：数学与系统科学研究院
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