116 research outputs found
Efficient Minimization of Higher Order Submodular Functions using Monotonic Boolean Functions
Submodular function minimization is a key problem in a wide variety of
applications in machine learning, economics, game theory, computer vision, and
many others. The general solver has a complexity of where is the time required to evaluate the function and
is the number of variables \cite{Lee2015}. On the other hand, many computer
vision and machine learning problems are defined over special subclasses of
submodular functions that can be written as the sum of many submodular cost
functions defined over cliques containing few variables. In such functions, the
pseudo-Boolean (or polynomial) representation \cite{BorosH02} of these
subclasses are of degree (or order, or clique size) where . In
this work, we develop efficient algorithms for the minimization of this useful
subclass of submodular functions. To do this, we define novel mapping that
transform submodular functions of order into quadratic ones. The underlying
idea is to use auxiliary variables to model the higher order terms and the
transformation is found using a carefully constructed linear program. In
particular, we model the auxiliary variables as monotonic Boolean functions,
allowing us to obtain a compact transformation using as few auxiliary variables
as possible
Quadratization of Symmetric Pseudo-Boolean Functions
A pseudo-Boolean function is a real-valued function
of binary variables; that is, a mapping from
to . For a pseudo-Boolean function on
, we say that is a quadratization of if is a
quadratic polynomial depending on and on auxiliary binary variables
such that for
all . By means of quadratizations, minimization of is
reduced to minimization (over its extended set of variables) of the quadratic
function . This is of some practical interest because minimization of
quadratic functions has been thoroughly studied for the last few decades, and
much progress has been made in solving such problems exactly or heuristically.
A related paper \cite{ABCG} initiated a systematic study of the minimum number
of auxiliary -variables required in a quadratization of an arbitrary
function (a natural question, since the complexity of minimizing the
quadratic function depends, among other factors, on the number of
binary variables). In this paper, we determine more precisely the number of
auxiliary variables required by quadratizations of symmetric pseudo-Boolean
functions , those functions whose value depends only on the Hamming
weight of the input (the number of variables equal to ).Comment: 17 page
Higher-Order Regularization in Computer Vision
At the core of many computer vision models lies the minimization of an objective function consisting of a sum of functions with few arguments. The order of the objective function is defined as the highest number of arguments of any summand. To reduce ambiguity and noise in the solution, regularization terms are included into the objective function, enforcing different properties of the solution. The most commonly used regularization is penalization of boundary length, which requires a second-order objective function. Most of this thesis is devoted to introducing higher-order regularization terms and presenting efficient minimization schemes. One of the topics of the thesis covers a reformulation of a large class of discrete functions into an equivalent form. The reformulation is shown, both in theory and practical experiments, to be advantageous for higher-order regularization models based on curvature and second-order derivatives. Another topic is the parametric max-flow problem. An analysis is given, showing its inherent limitations for large-scale problems which are common in computer vision. The thesis also introduces a segmentation approach for finding thin and elongated structures in 3D volumes. Using a line-graph formulation, it is shown how to efficiently regularize with respect to higher-order differential geometric properties such as curvature and torsion. Furthermore, an efficient optimization approach for a multi-region model is presented which, in addition to standard regularization, is able to enforce geometric constraints such as inclusion or exclusion of different regions. The final part of the thesis deals with dense stereo estimation. A new regularization model is introduced, penalizing the second-order derivatives of a depth or disparity map. Compared to previous second-order approaches to dense stereo estimation, the new regularization model is shown to be more easily optimized
Compact versus noncompact LP formulations for minimizing convex Choquet integrals
AbstractWe address here the problem of minimizing Choquet Integrals (also known as “Lovász Extensions”) over solution sets which can be either polyhedra or (mixed) integer sets. Typical applications of such problems concern the search of compromise solutions in multicriteria optimization. We focus here on the case where the Choquet Integrals to be minimized are convex, implying that the set functions (or “capacities”) underlying the Choquet Integrals considered are submodular. We first describe an approach based on a large scale LP formulation, and show how it can be handled via the so-called column-generation technique. We next investigate alternatives based on compact LP formulations, i.e. featuring a polynomial number of variables and constraints. Various potentially useful special cases corresponding to well-identified subclasses of underlying set functions are considered: quadratic and cubic submodular functions, and a more general class including set functions which, up to a sign, correspond to capacities which are both (k+1)−additive and k-monotone for k≥3. Computational experiments carried out on series of test instances, including transportation problems and knapsack problems, clearly confirm the superiority of compact formulations. As far as we know, these results represent the first systematic way of practically solving Choquet minimization problems on solution sets of significantly large dimensions
Quadratic reformulations of nonlinear binary optimization problems
Very large nonlinear unconstrained binary optimization problems arise in a broad array of applications. Several exact or heuristic techniques have proved quite successful for solving many of these problems when the objective function is a quadratic polynomial. However, no similarly efficient methods are available for the higher degree case. Since high degree objectives are becoming increasingly important in certain application areas, such as computer vision, various techniques have been recently developed to reduce the general case to the quadratic one, at the cost of increasing the number of variables. In this paper we initiate a systematic study of these quadratization approaches. We provide tight lower and upper bounds on the number of auxiliary variables needed in the worst-case for general objective functions, for bounded-degree functions, and for a restricted class of quadratizations. Our upper bounds are constructive, thus yielding new quadratization procedures. Finally, we completely characterize all ``minimal'' quadratizations of negative monomials
Discrete Optimization Methods for Segmentation and Matching
This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.
Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of . A branch and bound procedure is also presented to improve the solution computed by the algorithm.
The last part of the dissertation studies the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem, chamfer matching remains to be the preferred method when speed and robustness are considered. In this dissertation, I significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). It is achieved by incorporating edge orientation information in the matching algorithm so the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, I present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows one to bound the cost distribution of large neighborhoods and skip the bad hypotheses. Experiments show that the proposed approach improves the speed of the original chamfer matching up to an order of 45 times, and it is much faster than many state of art techniques while the accuracy is comparable. I further demonstrate the application of the proposed algorithm in providing seamless operation for a robotic bin picking system
Discrete graphical models -- an optimization perspective
This monograph is about discrete energy minimization for discrete graphical
models. It considers graphical models, or, more precisely, maximum a posteriori
inference for graphical models, purely as a combinatorial optimization problem.
Modeling, applications, probabilistic interpretations and many other aspects
are either ignored here or find their place in examples and remarks only. It
covers the integer linear programming formulation of the problem as well as its
linear programming, Lagrange and Lagrange decomposition-based relaxations. In
particular, it provides a detailed analysis of the polynomially solvable
acyclic and submodular problems, along with the corresponding exact
optimization methods. Major approximate methods, such as message passing and
graph cut techniques are also described and analyzed comprehensively. The
monograph can be useful for undergraduate and graduate students studying
optimization or graphical models, as well as for experts in optimization who
want to have a look into graphical models. To make the monograph suitable for
both categories of readers we explicitly separate the mathematical optimization
background chapters from those specific to graphical models.Comment: 270 page
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