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(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision
Author(s): Nikos Komodakis;M. Pawan Kumar;Nikos Paragios
Source: Journal:Foundations and Trends® in Computer Graphics and Vision ISSN Print:1572-2740, ISSN Online:1572-2759 Publisher:Now Publishers Volume 10 Number 1, Pages: 105(1-102) DOI: 10.1561/0600000066
Abstract:
Computational visual perception seeks to reproduce human vision
through the combination of visual sensors, artificial intelligence and
computing. To this end, computer vision tasks are often reformulated
as mathematical inference problems where the objective is to determine
the set of parameters corresponding to the lowest potential of a taskspecific
objective function. Graphical models have been the most popular
formulation in the field over the past two decades where the problem
is viewed as a discrete assignment labeling one. Modularity, scalability
and portability are the main strengths of these methods which once
combined with efficient inference algorithms they could lead to state of
the art results. In this tutorial we focus on the inference component of
the problem and in particular we discuss in a systematic manner the
most commonly used optimization principles in the context of graphical
models. Our study concerns inference over low rank models (interactions
between variables are constrained to pairs) as well as higher order
ones (arbitrary set of variables determine hyper-cliques on which constraints
are introduced) and seeks a concise, self-contained presentation
of prior art as well as the presentation of the current state of the art
methods in the field.
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