5 research outputs found
Higher-order Projected Power Iterations for Scalable Multi-Matching
The matching of multiple objects (e.g. shapes or images) is a fundamental
problem in vision and graphics. In order to robustly handle ambiguities, noise
and repetitive patterns in challenging real-world settings, it is essential to
take geometric consistency between points into account. Computationally, the
multi-matching problem is difficult. It can be phrased as simultaneously
solving multiple (NP-hard) quadratic assignment problems (QAPs) that are
coupled via cycle-consistency constraints. The main limitations of existing
multi-matching methods are that they either ignore geometric consistency and
thus have limited robustness, or they are restricted to small-scale problems
due to their (relatively) high computational cost. We address these
shortcomings by introducing a Higher-order Projected Power Iteration method,
which is (i) efficient and scales to tens of thousands of points, (ii)
straightforward to implement, (iii) able to incorporate geometric consistency,
(iv) guarantees cycle-consistent multi-matchings, and (iv) comes with
theoretical convergence guarantees. Experimentally we show that our approach is
superior to existing methods
Adaptively Transforming Graph Matching
Recently, many graph matching methods that incorporate pairwise constraint
and that can be formulated as a quadratic assignment problem (QAP) have been
proposed. Although these methods demonstrate promising results for the graph
matching problem, they have high complexity in space or time. In this paper, we
introduce an adaptively transforming graph matching (ATGM) method from the
perspective of functional representation. More precisely, under a
transformation formulation, we aim to match two graphs by minimizing the
discrepancy between the original graph and the transformed graph. With a linear
representation map of the transformation, the pairwise edge attributes of
graphs are explicitly represented by unary node attributes, which enables us to
reduce the space and time complexity significantly. Due to an efficient
Frank-Wolfe method-based optimization strategy, we can handle graphs with
hundreds and thousands of nodes within an acceptable amount of time. Meanwhile,
because transformation map can preserve graph structures, a domain
adaptation-based strategy is proposed to remove the outliers. The experimental
results demonstrate that our proposed method outperforms the state-of-the-art
graph matching algorithms
A Functional Representation for Graph Matching
Graph matching is an important and persistent problem in computer vision and
pattern recognition for finding node-to-node correspondence between
graph-structured data. However, as widely used, graph matching that
incorporates pairwise constraints can be formulated as a quadratic assignment
problem (QAP), which is NP-complete and results in intrinsic computational
difficulties. In this paper, we present a functional representation for graph
matching (FRGM) that aims to provide more geometric insights on the problem and
reduce the space and time complexities of corresponding algorithms. To achieve
these goals, we represent a graph endowed with edge attributes by a linear
function space equipped with a functional such as inner product or metric, that
has an explicit geometric meaning. Consequently, the correspondence between
graphs can be represented as a linear representation map of that functional.
Specifically, we reformulate the linear functional representation map as a new
parameterization for Euclidean graph matching, which is associative with
geometric parameters for graphs under rigid or nonrigid deformations. This
allows us to estimate the correspondence and geometric deformations
simultaneously. The use of the representation of edge attributes rather than
the affinity matrix enables us to reduce the space complexity by two orders of
magnitudes. Furthermore, we propose an efficient optimization strategy with low
time complexity to optimize the objective function. The experimental results on
both synthetic and real-world datasets demonstrate that the proposed FRGM can
achieve state-of-the-art performance