A Convex Relaxation for Multi-Graph Matching

Swoboda, Paul; Kainmüller, Dagmar; Mokarian, Ashkan; Theobalt, Christian; Bernard, Florian

doi:10.1109/cvpr.2019.01141

Cited by 34 publications

(20 citation statements)

References 39 publications

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“…Second, most papers changed diagrams into graphs [63] or ontologies [10] to calculate structural similarities. Then, they used graph matching [64,65,66,67,68] to measure similarity. Yuan [63] used the maximum common subgraph [69,70,71,72] as a similarity method.…”

Section: What Methods Are Used To Calculate the Similarity Between The Unified Modeling Language Diagrams Of Two Software Products?mentioning

confidence: 99%

Software similarity measurements using UML diagrams: A systematic literature review

Triandini

Fauzan

Siahaan

et al. 2021

regist. j. ilm. teknol. sist. inf.

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Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material.

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Section: What Methods Are Used To Calculate the Similarity Between The Unified Modeling Language Diagrams Of Two Software Products?mentioning

confidence: 99%

Software similarity measurements using UML diagrams: A systematic literature review

Triandini

Fauzan

Siahaan

et al. 2021

regist. j. ilm. teknol. sist. inf.

View full text Add to dashboard Cite

show abstract

“…Lastly, we point out that the multi-way data association problem can be viewed and solved from a graph matching perspective [19,20]. Unlike all previously discussed methods (and the present work), which only leverage the association information across views, graph matching additionally incorporates geometrical information between the items in each view.…”

Section: A Related Workmentioning

confidence: 99%

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association

Fathian¹,

Khosoussi²,

Tian³

et al. 2019

Preprint

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Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues. By leveraging insights from the multi-way matching and spectral graph clustering literatures, CLEAR provides cycle consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online. SUPPLEMENTARY MATERIALCLEAR source code and the code for generating comparison results: https://github.com/KavehFathian/clear.

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“…While some of these methods accept weighted inputs, they are formulated for synchronizing unimodal, binary associations. Lastly, when data has underlying structure, the formulation becomes a multi-graph matching problem [38], [39], [36], [40], which is considerably more computationally demanding.…”

Section: A Related Workmentioning

confidence: 99%

MIXER: A Principled Framework for Multimodal, Multiway Data Association

C.¹,

Roy²,

Fathian³

et al. 2021

Preprint

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A Convex Relaxation for Multi-Graph Matching

Cited by 34 publications

References 39 publications

Software similarity measurements using UML diagrams: A systematic literature review

Software similarity measurements using UML diagrams: A systematic literature review

CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association

MIXER: A Principled Framework for Multimodal, Multiway Data Association

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