A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number
Cheung, Yiu-ming; Li, Meng; Peng, Qinmu; Chen, C. L. Philip
2017-01
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume28Issue:1Pages:80-93
AbstractIt is usually hard to predetermine the true number of segments in lip segmentation. This paper, therefore, presents a clustering-based approach to lip segmentation without knowing the true segment number. The objective function in the proposed approach is a variant of the partition entropy (PE) and features that the coincident cluster centroids in pattern space can be equivalently substituted by one centroid with the function value unchanged. It is shown that the minimum of the proposed objective function can be reached provided that: 1) the number of positions occupied by cluster centroids in pattern space is equal to the true number of clusters and 2) these positions are coincident with the optimal cluster centroids obtained under PE criterion. In implementation, we first randomly initialize the clusters provided that the number of clusters is greater than or equal to the ground truth. Then, an iterative algorithm is utilized to minimize the proposed objective function. For each iterative step, not only is the winner, i.e., the centroid with the maximum membership degree, updated to adapt to the corresponding input data, but also the other centroids are adjusted with a specific cooperation strength, so that they are each close to the winner. Subsequently, the initial overpartition will be gradually faded out with the redundant centroids superposed over the convergence of the algorithm. Based upon the proposed algorithm, we present a lip segmentation scheme. Empirical studies have shown its efficacy in comparison with the existing methods.
KeywordClustering cooperative learning lip segmentation number of clusters
DOI10.1109/TNNLS.2015.2501547
URLView the original
Indexed BySCIE
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000391725000008
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Recommended Citation
GB/T 7714
Cheung, Yiu-ming,Li, Meng,Peng, Qinmu,et al. A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(1):80-93.
APA Cheung, Yiu-ming,Li, Meng,Peng, Qinmu,&Chen, C. L. Philip.(2017).A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(1),80-93.
MLA Cheung, Yiu-ming,et al."A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.1(2017):80-93.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Cheung, Yiu-ming]'s Articles
[Li, Meng]'s Articles
[Peng, Qinmu]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cheung, Yiu-ming]'s Articles
[Li, Meng]'s Articles
[Peng, Qinmu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cheung, Yiu-ming]'s Articles
[Li, Meng]'s Articles
[Peng, Qinmu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.