NDLI: Efficient Message Representations for Belief Propagation

Content Provider	IEEE Xplore Digital Library
Author	Tianli Yu Ruei-Sung Lin Super, B. Bei Tang
Copyright Year	2007
Description	Author affiliation: Motorola Labs, Schaumburg (Tianli Yu; Ruei-Sung Lin; Super, B.; Bei Tang)
Abstract	Belief propagation (BP) has been successfully used to approximate the solutions of various Markov random field (MRF) formulated energy minimization problems. However, large MRFs require a significant amount of memory to store the intermediate belief messages. We observe that these messages have redundant information due to the imposed smoothness prior. In this paper, we study the feasibility of applying compression techniques to the messages in the min-sum/max-product BP algorithm with 1D labels to improve the memory efficiency and reduce the read/write bandwidth. We articulate properties that an efficient message representation should satisfy. We investigate two common compression schemes, predictive coding and linear transform coding (PCA), and then propose a novel envelope point transform (EPT) method. Predictive coding is efficient and supports linear operations directly in the compressed domain, but it is only compatible with the $L_{1}$ smoothness function. PCA has the disadvantage that it does not guarantee the preservation of the minimal label. EPT is not limited to $L_{1}$ smoothness cost and allows a flexible quality vs. compression ratio tradeoff compared with predictive coding. Experiments on dense stereo reconstruction have shown that the predictive scheme and EPT can achieve 8times or more compression without significant loss of depth accuracy.
Starting Page	1
Ending Page	8
File Size	579095
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4408905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Belief propagation Predictive coding Bandwidth Convolution Embedded system Markov random fields Read-write memory Principal component analysis Costs Redundancy
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

Hardware-efficient belief propagation

Efficient belief propagation for early vision

Efficient Belief Propagation for Early Vision

Efficient Belief Propagation for Vision Using Linear Constraint Nodes

Canonical Face Depth Map: A Robust 3D Representation for Face Verification

Learning Conditional Random Fields for Stereo

Efficient piecewise learning for conditional random fields

Dense photometric stereo using tensorial belief propagation

Solving Markov Random Fields using Second Order Cone Programming Relaxations

Efficient Message Representations for Belief Propagation

Similar Documents

Hardware-efficient belief propagation

Efficient belief propagation for early vision

Efficient Belief Propagation for Early Vision

Efficient Belief Propagation for Vision Using Linear Constraint Nodes

Canonical Face Depth Map: A Robust 3D Representation for Face Verification

Learning Conditional Random Fields for Stereo

Efficient piecewise learning for conditional random fields

Dense photometric stereo using tensorial belief propagation

Solving Markov Random Fields using Second Order Cone Programming Relaxations

Efficient Message Representations for Belief Propagation