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|Title: ||Bayesian graph-theoretic approaches to image structure modeling and matching|
|Other Titles: ||Beiyesitu fang fa zai tu xiang jie gou jian mo yu pi pei zhong de ying yong|
|Authors: ||Feng, Wei (馮偉)|
|Department: ||School of Creative Media|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2008|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Image processing -- Digital techniques.|
Bayesian statistical decision theory.
|Notes: ||xvi, 195 leaves : ill. 30 cm.|
Thesis (Ph.D.)--City University of Hong Kong, 2008.
Includes bibliographical references (leaves 179-195)
CityU Call Number: TA1637 .F46 2008
|Abstract: ||Image structure modeling and matching remain two active topics in computer vision for
decades, and compose the foundation for many content-based applications in media computing.
Currently, most research on these two problems is usually conducted independently
with different methods. In this dissertation, we generalize image structure modeling
and matching with a unified paradigm, namely the Bayesian graph-theoretic approach.
We present a series of novel structural modeling and matching algorithms arising from the
Bayesian graph formulation.
This dissertation mainly addresses the following three problems: 1) How to extract a
reliable structural labeling using Bayesian decision theory, Markov random fields (MRFs)
and graph formulation? 2) How to construct a compact and effective structural model to
encode both global topology and salient local features? 3) How to efficiently match two
structural models under moderate occlusion and clutter?
Our work on the first problem focuses on integrating graph formulation and graphtheoretic
methods with MRFs and Bayesian model. The MRFs and Bayesian decision
theory provide a powerful tool for statistical image analysis and structural pattern modeling.
However, self-validation always remains an open problem for traditional methods.
Besides, the computational complexity of maximum a posteriori probability (MAP) estimation
is another critical weakness, leading to a large sacrifice of accuracy for speed.
Using Bayesian graph formulation and graph-theoretic methods, we proposed a general
paradigm, namely graduated graph mincuts (GGC) that is self-validated and significantly
reduce the computational complexity compared to flat MRF/MAP methods.
Within the GGC framework, we propose three concrete algorithms: tree-structured graph cuts (TSGC), net-structured graph cuts (NSGC) and hierarchical graph cuts (HGC). We
also generalize the discriminative spectral clustering to Bayesian spectral clustering by
combining with generative models.
We address the second problem based on the proposed structural labeling methods.
A reliable structural model called Bayesian structural content abstraction (BaSCA) is
proposed, which is an attributed graph representation of image structural content. The
BaSCA model is robust to non-content changing operations (NCOs) and sensitive to
content-changing operations (COs). In addition, to support dynamic NCO/CO partition,
we further optimize the BaSCA structure model in the user-defined NCO space with an
analogy mean shift algorithm, namely identical structure extension. Besides the robustness,
another nice property of the BaSCA model is that it can be extended naturally by
integrating salient local features. The BaSCA model is applied to region-level image authentication.
Experiments show that the BaSCA signature significantly improves the false
positive rate and has comparable false negative rates with previous methods.
We study the third problem, structural matching, with spectral graph theory. We show
how to handle the occlusion/clutter problem in the normalized eigenspace using a spectral
approach, which is considered difficult for classical spectral methods. We also discuss
the spectral multiplicity problem in graph matching, and propose a multiplicity-tolerant
algorithm for structural matching.
Finally, the proposed algorithms are applied to solving some real-world problems
in computer vision and media computing, such as media content authentication, automatic/
interactive segmentation, shape matching and retrieval etc. Some related problems,
e.g., multiple grouping cues extraction and shape structure abstraction, are also discussed.
The algorithms developed in this dissertation are not limited to two-dimensional Cartesian
spaces or any metric structure. Therefore, the Bayesian graph-theoretic approach
may readily find a large variety of applications in computer vision, pattern recognition
and media computing.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b2268730|
|Appears in Collections:||SCM - Doctor of Philosophy |
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