NDLI: Image segmentation by Dirichlet process mixture model with generalised mean

Content Provider	IET Digital Library
Author	Zhang, Hui Wu, Qing Ming Jonathan Nguyen, Thanh Minh
Abstract	The Dirichlet process mixture model (DPMM) with spatial constraints – e.g. hidden Markov random field (HMRF) model – has been considered as an effective algorithm for image processing application. However, the HMRF model is complex and time-consuming for implementation. A new DPMM has been introduced, where a generalised mean (GDM) is selected as the spatial constraints function. The GDM is applied not only on prior probability (and posterior probability) to incorporate local spatial information and component information, but also on conditional probability to incorporate local spatial information and observation information. The purpose of the HMRF model and GDM are the same for incorporating some spatial constraints into the system. However, compared to HMRF, GDM is easier, faster and simpler to implement. Finally, a variational Bayesian approach has been adopted for parameters estimation and model selection. Experimental results on image segmentation application demonstrate the improved performance of the proposed approach.
Starting Page	103
Ending Page	111
Page Count	9
ISSN	17519659
Volume Number	8
e-ISSN	17519667
Issue Number	Issue 2, Feb (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/8/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2013.0232
Journal	IET Image Processing
Publisher Date	2013-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Component Information Computer Vision And Image Processing Technique Conditional Probability Dirichlet Process Mixture Model DPMM GDM Generalised Mean Hidden Markov Model Hidden Markov Random Field Model HMRF Model Image Processing Application Image Segmentation Local Spatial Information Markov Processes Model Selection Observation Information Optical, Image And Video Signal Processing Parameter Estimation Posterior Probability Spatial Constraint Function Variational Bayesian Approach
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
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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