Graph Matching by Simplified Convex-Concave Relaxation Procedure
Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practica...
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| Autor*in: |
Liu, Zhi-Yong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2014 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 109(2014), 3 vom: 22. März, Seite 169-186 |
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| Übergeordnetes Werk: |
volume:109 ; year:2014 ; number:3 ; day:22 ; month:03 ; pages:169-186 |
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| DOI / URN: |
10.1007/s11263-014-0707-7 |
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| 520 | |a Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. | ||
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| 650 | 4 | |a Combinatorial optimization | |
| 650 | 4 | |a Deterministic annealing | |
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| 700 | 1 | |a Yang, Xu |4 aut | |
| 700 | 1 | |a Hoi, Steven C. H. |4 aut | |
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10.1007/s11263-014-0707-7 doi (DE-627)OLC2057748545 (DE-He213)s11263-014-0707-7-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Zhi-Yong verfasserin aut Graph Matching by Simplified Convex-Concave Relaxation Procedure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. Graph matching Combinatorial optimization Deterministic annealing Graduated optimization Feature correspondence Qiao, Hong aut Yang, Xu aut Hoi, Steven C. H. aut Enthalten in International journal of computer vision Springer US, 1987 109(2014), 3 vom: 22. März, Seite 169-186 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:109 year:2014 number:3 day:22 month:03 pages:169-186 https://doi.org/10.1007/s11263-014-0707-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 109 2014 3 22 03 169-186 |
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10.1007/s11263-014-0707-7 doi (DE-627)OLC2057748545 (DE-He213)s11263-014-0707-7-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Zhi-Yong verfasserin aut Graph Matching by Simplified Convex-Concave Relaxation Procedure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. Graph matching Combinatorial optimization Deterministic annealing Graduated optimization Feature correspondence Qiao, Hong aut Yang, Xu aut Hoi, Steven C. H. aut Enthalten in International journal of computer vision Springer US, 1987 109(2014), 3 vom: 22. März, Seite 169-186 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:109 year:2014 number:3 day:22 month:03 pages:169-186 https://doi.org/10.1007/s11263-014-0707-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 109 2014 3 22 03 169-186 |
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10.1007/s11263-014-0707-7 doi (DE-627)OLC2057748545 (DE-He213)s11263-014-0707-7-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Zhi-Yong verfasserin aut Graph Matching by Simplified Convex-Concave Relaxation Procedure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. Graph matching Combinatorial optimization Deterministic annealing Graduated optimization Feature correspondence Qiao, Hong aut Yang, Xu aut Hoi, Steven C. H. aut Enthalten in International journal of computer vision Springer US, 1987 109(2014), 3 vom: 22. März, Seite 169-186 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:109 year:2014 number:3 day:22 month:03 pages:169-186 https://doi.org/10.1007/s11263-014-0707-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 109 2014 3 22 03 169-186 |
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10.1007/s11263-014-0707-7 doi (DE-627)OLC2057748545 (DE-He213)s11263-014-0707-7-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Zhi-Yong verfasserin aut Graph Matching by Simplified Convex-Concave Relaxation Procedure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. Graph matching Combinatorial optimization Deterministic annealing Graduated optimization Feature correspondence Qiao, Hong aut Yang, Xu aut Hoi, Steven C. H. aut Enthalten in International journal of computer vision Springer US, 1987 109(2014), 3 vom: 22. März, Seite 169-186 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:109 year:2014 number:3 day:22 month:03 pages:169-186 https://doi.org/10.1007/s11263-014-0707-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 109 2014 3 22 03 169-186 |
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10.1007/s11263-014-0707-7 doi (DE-627)OLC2057748545 (DE-He213)s11263-014-0707-7-p DE-627 ger DE-627 rakwb eng 004 VZ Liu, Zhi-Yong verfasserin aut Graph Matching by Simplified Convex-Concave Relaxation Procedure 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. Graph matching Combinatorial optimization Deterministic annealing Graduated optimization Feature correspondence Qiao, Hong aut Yang, Xu aut Hoi, Steven C. H. aut Enthalten in International journal of computer vision Springer US, 1987 109(2014), 3 vom: 22. März, Seite 169-186 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:109 year:2014 number:3 day:22 month:03 pages:169-186 https://doi.org/10.1007/s11263-014-0707-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 109 2014 3 22 03 169-186 |
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Graph Matching by Simplified Convex-Concave Relaxation Procedure |
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Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. © Springer Science+Business Media New York 2014 |
| abstractGer |
Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. © Springer Science+Business Media New York 2014 |
| abstract_unstemmed |
Abstract The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms. © Springer Science+Business Media New York 2014 |
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Graph Matching by Simplified Convex-Concave Relaxation Procedure |
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Qiao, Hong Yang, Xu Hoi, Steven C. H. |
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