124 research outputs found
Optimization of Markov Random Fields in Computer Vision
A large variety of computer vision tasks can be formulated using
Markov Random Fields (MRF). Except in certain special cases,
optimizing an MRF is intractable, due to a large number of
variables and complex dependencies between them. In this thesis,
we present new algorithms to perform inference in MRFs, that are
either more efficient (in terms of running time and/or memory
usage) or more effective (in terms of solution quality), than the
state-of-the-art methods.
First, we introduce a memory efficient max-flow algorithm for
multi-label submodular MRFs. In fact,
such MRFs have been shown to be optimally solvable using max-flow
based on an encoding of the labels proposed by Ishikawa, in which
each variable is represented by nodes (where
is the number of labels) arranged in a column. However, this
method in general requires edges for each pair of
neighbouring variables. This makes it inapplicable to realistic
problems with many variables and labels, due to excessive memory
requirement. By contrast, our max-flow algorithm stores
values per variable pair, requiring much less storage.
Consequently, our algorithm makes it possible to optimally solve
multi-label submodular problems involving large numbers of
variables and labels on a standard computer.
Next, we present a move-making style algorithm for multi-label
MRFs with robust non-convex priors. In particular, our algorithm
iteratively approximates the original MRF energy with an
appropriately weighted surrogate energy that is easier to
minimize. Furthermore, it guarantees that the original energy
decreases at each iteration. To this end, we consider the
scenario where the weighted surrogate energy is multi-label
submodular (i.e., it can be optimally minimized by max-flow), and
show that our algorithm then lets us handle of a large variety of
non-convex priors.
Finally, we consider the fully connected Conditional Random Field
(dense CRF) with Gaussian pairwise potentials that has proven
popular and effective for multi-class semantic segmentation.
While the energy of a dense CRF can be minimized accurately using
a Linear Programming (LP) relaxation, the state-of-the-art
algorithm is too slow to be useful in practice. To alleviate this
deficiency, we introduce an efficient LP minimization algorithm
for dense CRFs. To this end, we develop a proximal minimization
framework, where the dual of each proximal problem is optimized
via block-coordinate descent. We show that each block of
variables can be optimized in a time linear in the number of
pixels and labels. Consequently, our algorithm enables efficient
and effective optimization of dense CRFs with Gaussian pairwise
potentials.
We evaluated all our algorithms on standard energy minimization
datasets consisting of computer vision problems, such as stereo,
inpainting and semantic segmentation. The experiments at the end
of each chapter provide compelling evidence that all our
approaches are either more efficient or more effective than all
existing baselines
An ILP Solver for Multi-label MRFs with Connectivity Constraints
Integer Linear Programming (ILP) formulations of Markov random fields (MRFs)
models with global connectivity priors were investigated previously in computer
vision, e.g., \cite{globalinter,globalconn}. In these works, only Linear
Programing (LP) relaxations \cite{globalinter,globalconn} or simplified
versions \cite{graphcutbase} of the problem were solved. This paper
investigates the ILP of multi-label MRF with exact connectivity priors via a
branch-and-cut method, which provably finds globally optimal solutions. The
method enforces connectivity priors iteratively by a cutting plane method, and
provides feasible solutions with a guarantee on sub-optimality even if we
terminate it earlier. The proposed ILP can be applied as a post-processing
method on top of any existing multi-label segmentation approach. As it provides
globally optimal solution, it can be used off-line to generate ground-truth
labeling, which serves as quality check for any fast on-line algorithm.
Furthermore, it can be used to generate ground-truth proposals for weakly
supervised segmentation. We demonstrate the power and usefulness of our model
by several experiments on the BSDS500 and PASCAL image dataset, as well as on
medical images with trained probability maps.Comment: 19 page
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Maximum a posteriori (MAP) inference over discrete Markov random fields is a
fundamental task spanning a wide spectrum of real-world applications, which is
known to be NP-hard for general graphs. In this paper, we propose a novel
semidefinite relaxation formulation (referred to as SDR) to estimate the MAP
assignment. Algorithmically, we develop an accelerated variant of the
alternating direction method of multipliers (referred to as SDPAD-LR) that can
effectively exploit the special structure of the new relaxation. Encouragingly,
the proposed procedure allows solving SDR for large-scale problems, e.g.,
problems on a grid graph comprising hundreds of thousands of variables with
multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable
of attaining comparable accuracy while exhibiting remarkably improved
scalability, in contrast to the commonly held belief that semidefinite
relaxation can only been applied on small-scale MRF problems. We have evaluated
the performance of SDR on various benchmark datasets including OPENGM2 and PIC
in terms of both the quality of the solutions and computation time.
Experimental results demonstrate that for a broad class of problems, SDPAD-LR
outperforms state-of-the-art algorithms in producing better MAP assignment in
an efficient manner.Comment: accepted to International Conference on Machine Learning (ICML 2014
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