Bayesian Networks for Image Understanding

Bayesian Networks for Image Understanding

Andreas Savaki (Rochester Institute of Technology, USA), Jiebo Luo (Kodak Research Laboratories, USA) and Michael Kane (Yale University, USA)
Copyright: © 2007 |Pages: 23
DOI: 10.4018/978-1-59904-141-4.ch007
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Abstract

Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that Bayesian networks are particularly well-suited for image understanding problems, and present case studies in indoor-outdoor scene classification and parts-based object detection. First, improved scene classification is accomplished using both low-level features, such as color and texture, and semantic features, such as the presence of sky and grass. Integration of low-level and semantic features is achieved using a Bayesian network framework. The network structure can be determined by expert opinion or by automated structure learning methods. Second, object detection at multiple views relies on a parts-based approach, where specialized detectors locate object parts and a Bayesian network acts as the arbitrator in order to determine the object presence. In general, Bayesian networks are found to be powerful integrators of different features and help improve the performance of image understanding systems.

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Table of Contents
Foreword
K. R. Rao
Chapter 1
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, Zhangbing Zhou
Naive Bayesian network (NB) is a simple yet powerful Bayesian network. Even with a strong independency assumption among the features, it... Sample PDF
A Novel Discriminative Naive Bayesian Network for Classification
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Chapter 2
Ben K. Daniel, Juan-Diego Zapata-Rivera, Gordon I. McCalla
Bayesian belief networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques... Sample PDF
A Bayesian Belief Network Approach for Modeling Complex Domains
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Chapter 3
Sachin Shetty, Min Song, Mansoor Alam
A Bayesian network model is a popular formalism for data mining due to its intuitive interpretation. This chapter presents a semantic genetic... Sample PDF
Data Mining of Bayesian Network Structure Using a Semantic Genetic Algorithm-Based Approach
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Chapter 4
Dimitris Margaritis, Christos Faloutsos, Sebastian Thrun
We present a novel method for answering count queries from a large database approximately and quickly. Our method implements an approximate DataCube... Sample PDF
NetCube: Fast, Approximate Database Queries Using Bayesian Networks
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Chapter 5
Helge Langseth, Luigi Portinale
Over the last decade, Bayesian networks (BNs) have become a popular tool for modeling many kinds of statistical problems. In this chapter we will... Sample PDF
Applications of Bayesian Networks in Reliability Analysis
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Chapter 6
Sumeet Gupta, Hee-Wong Kim
This chapter deals with the application of Bayesian modeling as a management decision support tool for management information systems (MIS)... Sample PDF
Application of Bayesian Modeling to Management Information Systems: A Latent Scores Approach
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Chapter 7
Andreas Savaki, Jiebo Luo, Michael Kane
Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that... Sample PDF
Bayesian Networks for Image Understanding
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Chapter 8
Pedro M. Jorge, Arnaldo J. Abrantes, João M. Lemos, Jorge S. Marques
This chapter describes an algorithm for tracking groups of pedestrians in video sequences. The main difficulties addressed in this work concern... Sample PDF
Long Term Tracking of Pedestrians with Groups and Occlusions
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Chapter 9
Qian Diao, Jianye Lu, Wei Hu, Yimin Zhang, Gary Bradski
In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background... Sample PDF
DBN Models for Visual Tracking and Prediction
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Chapter 10
David Lo
In applications where the locations of human subjects are needed, for example, human-computer interface, video conferencing, and security... Sample PDF
Multimodal Human Localization Using Bayesian Network Sensor Fusion
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Chapter 11
C. Notarnicola
This chapter introduces the use of Bayesian methodology for inversion purposes: the extraction of bio-geophysical parameters from remotely sensed... Sample PDF
Retrieval of Bio-Geophysical Parameters from Remotely Sensing Data by Using Bayesian Methodology
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Chapter 12
Arunkumar Chinnasamy, Sudhanshu Patwardhan, Wing-Kin Sung
The end of the 20th century and the advent of the new millennium have brought in a true merger of sciences for the benefit of mankind. The biggest... Sample PDF
Application of Bayesian Network in Drug Discovery and Development Process
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Chapter 13
Seiya Imoto, Satoru Miyano
In cells, genes interact with each other and this system can be viewed as directed graphs. A gene network is a graphical representation of... Sample PDF
Bayesian Network Approach to Estimate Gene Networks
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Chapter 14
Vipin Narang, Rajesh Chowdhary, Ankush Mittal, Wing-Kin Sung
A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to... Sample PDF
Bayesian Network Modeling of Transcription Factor Binding Sites: A Tutorial
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Chapter 15
Tie-Fei Liu, Wing-Kin Sung, Ankush Mittal
Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Currently... Sample PDF
Application of Bayesian Network in Learning Gene Network
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