NDLI: Learning Bayesian Networks with qualitative constraints

Content Provider	IEEE Xplore Digital Library
Author	Yan Tong Qiang Ji
Copyright Year	2008
Description	Author affiliation: GE Global Res. Center, Niskayuna, NY (Yan Tong)
Abstract	Graphical models such as Bayesian networks (BNs) are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data, which proves to be difficult to acquire for many computer vision tasks. On the other hand, there is often available qualitative prior knowledge about the model. Such knowledge comes either from domain experts based on their experience or from various physical or geometric constraints that govern the objects we try to model. Unlike the quantitative prior, the qualitative prior is often ignored due to the difficulty of incorporating them into the model learning process. In this paper, we introduce a closed-form solution to systematically combine the limited training data with some generic qualitative knowledge for BN parameter learning. To validate our method, we compare it with the maximum likelihood (ML) estimation method under sparse data and with the expectation maximization (EM) algorithm under incomplete data respectively. To further demonstrate its applications for computer vision, we apply it to learn a BN model for facial action unit (AU) recognition from real image data. The experimental results show that with simple and generic qualitative constraints and using only a small amount of training data, our method can robustly and accurately estimate the BN model parameters.
Starting Page	1
Ending Page	8
File Size	318815
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Computer vision Training data Maximum likelihood estimation Graphical models Computer network reliability Solid modeling Closed-form solution Application software Gold
Content Type	Text
Resource Type	Article
Subject	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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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