2022
DOI: 10.1007/978-3-031-20050-2_37
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A Comparative Study of Graph Matching Algorithms in Computer Vision

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Cited by 9 publications
(22 citation statements)
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“…For practical purposes, especially in computer vision [17], QAP can be generalized to the setting where the sets do not have the same size and some elements may remain unassigned (possibly for some cost). This gives rise to the incomplete QAP (IQAP) where one seeks an injective partial mapping (also called partial bijection) from one set to another that minimizes an objective consisting of both unary and binary functions, as in QAP.…”
Section: Related Workmentioning
confidence: 99%
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“…For practical purposes, especially in computer vision [17], QAP can be generalized to the setting where the sets do not have the same size and some elements may remain unassigned (possibly for some cost). This gives rise to the incomplete QAP (IQAP) where one seeks an injective partial mapping (also called partial bijection) from one set to another that minimizes an objective consisting of both unary and binary functions, as in QAP.…”
Section: Related Workmentioning
confidence: 99%
“…Again, if the objective of IQAP is restricted to contain only unary functions, one obtains the polynomially solvable incomplete LAP (ILAP). The IQAP has been formally defined and a reduction between it and QAP has been shown in [17,Section A1]. However, such a straightforward reduction (based on introducing multiple additional 'dummy' labels) need not be competitive with tackling IQAP directly if the underlying problem is large-scale and sparse.…”
Section: Related Workmentioning
confidence: 99%
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