Graduated Consistency-Regularized Optimization for Multi-graph Matching

Yan, Junchi; Li, Yin; Liu, Wei; Zha, Hongyuan; Yang, Xiaokang; Chu, Stephen M.

doi:10.1007/978-3-319-10590-1_27

Cited by 48 publications

(36 citation statements)

References 49 publications

(94 reference statements)

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“…Some recent work proposed to use the cycle consistency as a constraint to match a bunch of images [29,37,10]. The cycle consistency can be described by…”

Section: Cycle Consistencymentioning

confidence: 99%

Multi-image Matching via Fast Alternating Minimization

Zhou

Zhu

Daniilidis

2015

2015 IEEE International Conference on Computer Vision (ICCV)

135

158

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In this paper we propose a global optimization-based approach to jointly matching a set of images. The estimated correspondences simultaneously maximize pairwise feature affinities and cycle consistency across multiple images. Unlike previous convex methods relying on semidefinite programming, we formulate the problem as a low-rank matrix recovery problem and show that the desired semidefiniteness of a solution can be spontaneously fulfilled. The low-rank formulation enables us to derive a fast alternating minimization algorithm in order to handle practical problems with thousands of features. Both simulation and real experiments demonstrate that the proposed algorithm can achieve a competitive performance with an order of magnitude speedup compared to the state-of-the-art algorithm. In the end, we demonstrate the applicability of the proposed method to match the images of different object instances and as a result the potential to reconstruct category-specific object models from those images.

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“…Some recent work proposed to use the cycle consistency as a constraint to match a bunch of images [29,37,10]. The cycle consistency can be described by…”

Section: Cycle Consistencymentioning

confidence: 99%

Multi-image Matching via Fast Alternating Minimization

Zhou

Zhu

Daniilidis

2015

2015 IEEE International Conference on Computer Vision (ICCV)

135

158

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“…To evaluate the proposed algorithm, we performed two experiments using the synthetic and WILLOW datasets [31]. 1 We compare our algorithm with the well-known pairwise graph matching algorithms such as reweighted random walks matching (RRWM) [3], and multi-layer random walk matching (MLRWM) [28], and multiple graph matching algorithms such as MatchOpt (MOpt) [13,14], MatchSync (MSync) [24], Composition based affinity optimization (CAO) [15,16], MatchLift (MLift) [22], MatchALS (MALS) [26], and MatchEIG (MEIG) [23].…”

Section: Resultsmentioning

confidence: 99%

Consistent multiple graph matching with multi-layer random walks synchronization

Park

Yoon

2019

Pattern Recognition Letters

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We address the correspondence search problem among multiple graphs with complex properties while considering the matching consistency. We describe each pair of graphs by combining multiple attributes, then jointly match them in a unified framework. The main contribution of this paper is twofold. First, we formulate the global correspondence search problem of multi-attributed graphs by utilizing a set of multi-layer structures. The proposed formulation describes each pair of graphs as a multi-layer structure, and jointly considers whole matching pairs. Second, we propose a robust multiple graph matching method based on the multi-layer random walks framework. The proposed framework synchronizes movements of random walkers, and leads them to consistent matching candidates. In our extensive experiments, the proposed method exhibits robust and accurate performance over the state-of-the-art multiple graph matching algorithms.

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“…The latter employs spectral approximation to eigenvector decomposition on the matching configuration matrix stacked by all raw pairwise matching solutions (assignment matrix), and recover the consistent matching solutions. There are also several other more recent work on matching a batch of graphs by self-boosting [48] and multi-view point registration [49].…”

Section: B Advances On Multiple Graph Matchingmentioning

confidence: 99%

Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach

Yan

Wang

Zha

et al. 2015

IEEE Trans. on Image Process.

Self Cite

102

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The problem of graph matching (GM) in general is nondeterministic polynomial-complete and many approximate pairwise matching techniques have been proposed. For a general setting in real applications, it typically requires to find the consistent matching across a batch of graphs. Sequentially performing pairwise matching is prone to error propagation along the pairwise matching sequence, and the sequences generated in different pairwise matching orders can lead to contradictory solutions. Motivated by devising a robust and consistent multiple-GM model, we propose a unified alternating optimization framework for multi-GM. In addition, we define and use two metrics related to graphwise and pairwise consistencies. The former is used to find an appropriate reference graph, which induces a set of basis variables and launches the iteration procedure. The latter defines the order in which the considered graphs in the iterations are manipulated. We show two embodiments under the proposed framework that can cope with the nonfactorized and factorized affinity matrix, respectively. Our multi-GM model has two major characters: 1) the affinity information across multiple graphs are explored in each iteration by fixing part of the matching variables via a consistency-driven mechanism and 2) the framework is flexible to incorporate various existing pairwise GM solvers in an out-of-box fashion, and also can proceed with the output of other multi-GM methods. The experimental results on both synthetic data and real images empirically show that the proposed framework performs competitively with the state-of-the-art.

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Graduated Consistency-Regularized Optimization for Multi-graph Matching

Cited by 48 publications

References 49 publications

Multi-image Matching via Fast Alternating Minimization

Multi-image Matching via Fast Alternating Minimization

Consistent multiple graph matching with multi-layer random walks synchronization

Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach

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