NDLI: Incorporating Mean Template Into Finite Mixture Model for Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Hui Zhang Wu, Q.M.J. Thanh Minh Nguyen
Copyright Year	2012
Abstract	The well-known finite mixture model (FMM) has been regarded as a useful tool for image segmentation application. However, the pixels in FMM are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account. These limitations make the FMM more sensitive to noise. In this brief, we propose a simple and effective method to make the traditional FMM more robust to noise with the help of a mean template. FMM can be considered a linear combination of prior and conditional probability from the expression of its mathematical formula. We calculate these probabilities with two mean templates: a weighted arithmetic mean template and a weighted geometric mean template. Thus, in our model, the prior probability (or conditional probability) of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood to incorporate the local spatial and intensity information for eliminating the noise. Finally, our algorithm is general enough and can be extended to any other FMM-based models to achieve super performance. Experimental results demonstrate the improved robustness and effectiveness of our approach.
Page Count	8
File Size	732720
Starting Page	328
Ending Page	335
File Format	PDF
ISSN	2162237X
Volume Number	24
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probability Noise Image segmentation Robustness Approximation methods Nickel Learning systems spatial constraints Expectation maximization (EM) algorithm finite mixture model image segmentation mean template
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Synthetic Aperture Radar Image Segmentation by Modified Student's t-Mixture Model

Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation

Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

A Nonsymmetric Mixture Model for Unsupervised Image Segmentation

A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation

Detection of Compound Structures Using a Gaussian Mixture Model With Spectral and Spatial Constraints

Hierarchical Similarity Transformations Between Gaussian Mixtures

Regularized Mixture Density Estimation With an Analytical Setting of Shrinkage Intensities

Incorporating Mean Template Into Finite Mixture Model for Image Segmentation

Similar Documents

Synthetic Aperture Radar Image Segmentation by Modified Student's t-Mixture Model

Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation

Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

A Nonsymmetric Mixture Model for Unsupervised Image Segmentation

A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation

Detection of Compound Structures Using a Gaussian Mixture Model With Spectral and Spatial Constraints

Hierarchical Similarity Transformations Between Gaussian Mixtures

Regularized Mixture Density Estimation With an Analytical Setting of Shrinkage Intensities

Incorporating Mean Template Into Finite Mixture Model for Image Segmentation