NDLI: The infinite Hidden Markov random field model

Content Provider	IEEE Xplore Digital Library
Author	Chatzis, S.P. Tsechpenakis, G.
Copyright Year	2009
Abstract	Dirichlet process (DP) mixture models have recently emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Hidden Markov random field (HMRF) models are parametric statistical models widely used for image segmentation, as they appear naturally in problems where a spatially-constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e. the number of segments derived by the image segmentation procedure. Typically, for this purpose, various likelihood based criteria are employed. Nevertheless, such methods often fail to yield satisfactory results, exhibiting significant overfitting proneness. Recently, higher order conditional random field models using potentials defined on superpixels have been considered as alternatives tackling these issues. Still, these models are in general computationally inefficient, a fact that limits their widespread adoption in practical applications. To resolve these issues, in this paper we introduce a novel, nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model. We describe an efficient variational Bayesian inference algorithm for the proposed model, and we apply it to a series of image segmentation problems, demonstrating its advantages over existing methodologies.
Starting Page	654
Ending Page	661
File Size	573901
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Image segmentation Bayesian methods Inference algorithms Belief propagation Markov random fields Parametric statistics Pixel Yield estimation Approximation methods
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition 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

Bayesian image segmentation based on an inhomogenous hidden Markov random field

The Infinite Hidden Markov Random Field Model

Bayesian Stereo Matching

A Bayesian segmentation methodology for parametric image models

Dynamic background discrimination with belief propagation

Efficient belief propagation for early vision

Dense stereo matching using kernel maximum likelihood estimation

Learning real-time MRF inference for image denoising

Learning Topic Models by Belief Propagation

The infinite Hidden Markov random field model

Similar Documents

Bayesian image segmentation based on an inhomogenous hidden Markov random field

The Infinite Hidden Markov Random Field Model

Bayesian Stereo Matching

A Bayesian segmentation methodology for parametric image models

Dynamic background discrimination with belief propagation

Efficient belief propagation for early vision

Dense stereo matching using kernel maximum likelihood estimation

Learning real-time MRF inference for image denoising

Learning Topic Models by Belief Propagation

The infinite Hidden Markov random field model