Discriminatively boosted image clustering with fully convolutional auto-encoders
Li, Fengfu1,3; Qiao, Hong4,5,6; Zhang, Bo2,3
Source PublicationPATTERN RECOGNITION
2018-11-01
Volume83Pages:161-173
SubtypeArticle
AbstractTraditional 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.
KeywordImage Clustering Fully Convolutional Auto-encoder Representation Learning Discriminatively Boosted Clustering
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2018.05.019
WOS KeywordNEURAL-NETWORKS ; DEEP ; REPRESENTATIONS ; SEGMENTATION
Indexed BySCI
Language英语
Funding OrganizationNNSF of China(91648205 ; 61627808 ; 61602483 ; 61603389)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000442172200012
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21862
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.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
Recommended Citation
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.
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