Discriminatively boosted image clustering with fully convolutional auto-encoders
Li, Fengfu1,3; Qiao, Hong4,5,6; Zhang, Bo2,3
2018-11-01
发表期刊PATTERN RECOGNITION
卷号83页码:161-173
文章类型Article
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.
关键词Image Clustering Fully Convolutional Auto-encoder Representation Learning Discriminatively Boosted Clustering
WOS标题词Science & Technology ; Technology
DOI10.1016/j.patcog.2018.05.019
关键词[WOS]NEURAL-NETWORKS ; DEEP ; REPRESENTATIONS ; SEGMENTATION
收录类别SCI
语种英语
项目资助者NNSF of China(91648205 ; 61627808 ; 61602483 ; 61603389)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000442172200012
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21862
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Li, Fengfu]的文章
[Qiao, Hong]的文章
[Zhang, Bo]的文章
百度学术
百度学术中相似的文章
[Li, Fengfu]的文章
[Qiao, Hong]的文章
[Zhang, Bo]的文章
必应学术
必应学术中相似的文章
[Li, Fengfu]的文章
[Qiao, Hong]的文章
[Zhang, Bo]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

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。