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Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm
Yu SQ(余思泉)1,2,3; Shen GP(沈贵萍)4; Wang PY(王佩瑶)5; Wang YN(王宇宁)6; Wu CD(吴成东)7
Department机器人学研究室
Source PublicationIEEE ACCESS
ISSN2169-3536
2020
Volume8Pages:160285-160296
Indexed BySCI ; EI
EI Accession number20203909230403
WOS IDWOS:000571046600001
Contribution Rank1
KeywordClustering algorithms Mutual information Image representation Neural networks Clustering methods Feature extraction Task analysis Representation learning image clustering mutual information maximization self-training algorithm
Abstract

Image clustering is a complex procedure that is significantly affected by the choice of the image representation. Generally speaking, image representations are generated by using handcraft features or trained neural networks. When dealing with high dimension data, these two representation methods cause two problems: i) the representation ability of the manually designed features is limited; ii) the non-representative and meaningless feature of a trained deep network may hurt the clustering performance. To overcome these problems, we propose a new clustering method which efficiently builds an image representation and precisely discovers the cluster assignments. Our main tools are an unsupervised representation learning method based on Deep Mutual Information Maximization (DMIM) system, and a clustering method based on self-training algorithm. Specifically speaking, to extract the informative representation of image data, we derive the maximum mutual information theory and propose a system to learn the maximum mutual information between the input images and the latent representations. To discover the clusters and assign each image a clustering label, a self-training mechanism is applied to cluster the learned representations. The superiority and validity of our algorithm are verified in a series of real-world image clustering experiments.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27678
Collection机器人学研究室
Corresponding AuthorShen GP(沈贵萍)
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.College of Articial Intelligence, Yango University, Fuzhou 350108, China
5.Shenyang Institute of Technology, Shenyang 110000, China
6.PLA 32681, Shenyang 110000, China
7.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
Recommended Citation
GB/T 7714
Yu SQ,Shen GP,Wang PY,et al. Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm[J]. IEEE ACCESS,2020,8:160285-160296.
APA Yu SQ,Shen GP,Wang PY,Wang YN,&Wu CD.(2020).Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm.IEEE ACCESS,8,160285-160296.
MLA Yu SQ,et al."Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm".IEEE ACCESS 8(2020):160285-160296.
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