NDLI: Discriminative Random Fields

Content Provider	Springer Nature Link
Author	Kumar, Sanjiv Hebert, Martial
Copyright Year	2006
Abstract	In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by lafferty et al.(2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results.
Starting Page	179
Ending Page	201
Page Count	23
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	68
Issue Number	2
e-ISSN	15731405
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-04-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image Classification Spatial Interactions Markov Random Field Discriminative Classifiers Graphical Models Pattern Recognition Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence 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

Shape-Based Object Localization for Descriptive Classification

Fields of Experts

Learning Sparse FRAME Models for Natural Image Patterns

Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field

Nonparametric Bayesian Image Segmentation

Learning Dictionary of Discriminative Part Detectors for Image Categorization and Cosegmentation

On the Spatial Statistics of Optical Flow

Minimizing Energies with Hierarchical Costs

Inter-Image Statistics for 3D Environment Modeling

Discriminative Random Fields

Similar Documents

Shape-Based Object Localization for Descriptive Classification

Fields of Experts

Learning Sparse FRAME Models for Natural Image Patterns

Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field

Nonparametric Bayesian Image Segmentation

Learning Dictionary of Discriminative Part Detectors for Image Categorization and Cosegmentation

On the Spatial Statistics of Optical Flow

Minimizing Energies with Hierarchical Costs

Inter-Image Statistics for 3D Environment Modeling

Discriminative Random Fields