Discriminatively boosted image clustering with fully convolutional auto-encoders

doi:10.1016/j.patcog.2018.05.019

CSpace > 应用数学研究所

	Discriminatively boosted image clustering with fully convolutional auto-encoders
	Li, Fengfu 1,3; Qiao, Hong 4,5,6; Zhang, Bo2,3
	2018-11-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	83 页码:161-173
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
DOI	10.1016/j.patcog.2018.05.019
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000442172200012
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/31093
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.