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Discriminatively boosted image clustering with fully convolutional auto-encoders
Li, Fengfu1,3; Qiao, Hong4,5,6; Zhang, Bo2,3
2018-11-01
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
Volume83Pages:161-173
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
DOI10.1016/j.patcog.2018.05.019
Language英语
Funding ProjectNNSF of China[91648205] ; NNSF of China[61627808] ; NNSF of China[61602483] ; NNSF of China[61603389]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000442172200012
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31093
Collection应用数学研究所
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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