NDLI: Development of a global batch clustering with gradient descent and initial parameters in colour image classification

Content Provider	IET Digital Library
Author	Li, Peilin Lee, Sang Heon Park, Jae Sam
Abstract	This study addresses two issues from batch clustering using K-means algorithm in colour image classification application. One of the major issues is the drifting phenomenon in the batch clustering due to the stochastic nature of the clustering procedure. Also in literature, the initial parameter is important to direct the clustering algorithm converge to the proper local solution. In this study, a new algorithm is proposed to address these two issues in application. Recently, a research found that the principal component analysis (PCA) result directly indicates the membership of the clusters in K-means algorithm. Hence using this, the first part of the proposed algorithm shows the possibility to estimate the initial parameters accurately for K-means with a hierarchical manner of PCA solution. In addition, a gradient descent approach is used for the global batch clustering to reduce the drifting and hence speed up convergence in the refining stage. All necessary proofs and justifications are also provided. The evaluation study has shown that the proposed algorithm performs better than the original K-means clustering algorithms with various initial parameter estimation processes.
Starting Page	161
Ending Page	174
Page Count	14
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5956
Journal	IET Image Processing
Publisher Date	2018-10-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Clustering Algorithm Clustering Procedure Colour Image Classification Application Computer Vision And Image Processing Technique Drifting Phenomenon Global Batch Clustering Gradient Descent Approach Gradient Method Image Classification Image Colour Analysis Image Recognition Initial Parameter Estimation Processes Interpolation And Function Approximation K-means Algorithm Numerical Analysis Parameter Estimation Pattern Clustering PCA Principal Component Analysis Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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