Local commute-time guided MDS for 3D non-rigid object retrieval
Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haj Mohamed, Hela [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
Non-rigid 3D shape deformations |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 48(2018), 9 vom: 09. Jan., Seite 2873-2883 |
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| Übergeordnetes Werk: |
volume:48 ; year:2018 ; number:9 ; day:09 ; month:01 ; pages:2873-2883 |
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| DOI / URN: |
10.1007/s10489-017-1114-x |
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OLC206610504X |
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| 520 | |a Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. | ||
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| 650 | 4 | |a 3D canonical forms | |
| 650 | 4 | |a Multidimensional scaling | |
| 650 | 4 | |a 3D non-rigid shape retrieval | |
| 650 | 4 | |a Intrinsic metrics on 3D shapes | |
| 650 | 4 | |a Commute-time distance | |
| 700 | 1 | |a Belaid, Samir |4 aut | |
| 700 | 1 | |a Naanaa, Wady |4 aut | |
| 700 | 1 | |a Ben Romdhane, Lotfi |4 aut | |
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10.1007/s10489-017-1114-x doi (DE-627)OLC206610504X (DE-He213)s10489-017-1114-x-p DE-627 ger DE-627 rakwb eng 004 VZ Haj Mohamed, Hela verfasserin (orcid)0000-0002-1430-0878 aut Local commute-time guided MDS for 3D non-rigid object retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. Non-rigid 3D shape deformations 3D canonical forms Multidimensional scaling 3D non-rigid shape retrieval Intrinsic metrics on 3D shapes Commute-time distance Belaid, Samir aut Naanaa, Wady aut Ben Romdhane, Lotfi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 09. Jan., Seite 2873-2883 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:09 month:01 pages:2873-2883 https://doi.org/10.1007/s10489-017-1114-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 09 01 2873-2883 |
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10.1007/s10489-017-1114-x doi (DE-627)OLC206610504X (DE-He213)s10489-017-1114-x-p DE-627 ger DE-627 rakwb eng 004 VZ Haj Mohamed, Hela verfasserin (orcid)0000-0002-1430-0878 aut Local commute-time guided MDS for 3D non-rigid object retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. Non-rigid 3D shape deformations 3D canonical forms Multidimensional scaling 3D non-rigid shape retrieval Intrinsic metrics on 3D shapes Commute-time distance Belaid, Samir aut Naanaa, Wady aut Ben Romdhane, Lotfi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 09. Jan., Seite 2873-2883 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:09 month:01 pages:2873-2883 https://doi.org/10.1007/s10489-017-1114-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 09 01 2873-2883 |
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10.1007/s10489-017-1114-x doi (DE-627)OLC206610504X (DE-He213)s10489-017-1114-x-p DE-627 ger DE-627 rakwb eng 004 VZ Haj Mohamed, Hela verfasserin (orcid)0000-0002-1430-0878 aut Local commute-time guided MDS for 3D non-rigid object retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. Non-rigid 3D shape deformations 3D canonical forms Multidimensional scaling 3D non-rigid shape retrieval Intrinsic metrics on 3D shapes Commute-time distance Belaid, Samir aut Naanaa, Wady aut Ben Romdhane, Lotfi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 09. Jan., Seite 2873-2883 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:09 month:01 pages:2873-2883 https://doi.org/10.1007/s10489-017-1114-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 09 01 2873-2883 |
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10.1007/s10489-017-1114-x doi (DE-627)OLC206610504X (DE-He213)s10489-017-1114-x-p DE-627 ger DE-627 rakwb eng 004 VZ Haj Mohamed, Hela verfasserin (orcid)0000-0002-1430-0878 aut Local commute-time guided MDS for 3D non-rigid object retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. Non-rigid 3D shape deformations 3D canonical forms Multidimensional scaling 3D non-rigid shape retrieval Intrinsic metrics on 3D shapes Commute-time distance Belaid, Samir aut Naanaa, Wady aut Ben Romdhane, Lotfi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 09. Jan., Seite 2873-2883 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:09 month:01 pages:2873-2883 https://doi.org/10.1007/s10489-017-1114-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 09 01 2873-2883 |
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10.1007/s10489-017-1114-x doi (DE-627)OLC206610504X (DE-He213)s10489-017-1114-x-p DE-627 ger DE-627 rakwb eng 004 VZ Haj Mohamed, Hela verfasserin (orcid)0000-0002-1430-0878 aut Local commute-time guided MDS for 3D non-rigid object retrieval 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. Non-rigid 3D shape deformations 3D canonical forms Multidimensional scaling 3D non-rigid shape retrieval Intrinsic metrics on 3D shapes Commute-time distance Belaid, Samir aut Naanaa, Wady aut Ben Romdhane, Lotfi aut Enthalten in Applied intelligence Springer US, 1991 48(2018), 9 vom: 09. Jan., Seite 2873-2883 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2018 number:9 day:09 month:01 pages:2873-2883 https://doi.org/10.1007/s10489-017-1114-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2018 9 09 01 2873-2883 |
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Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Local commute-time guided MDS for 3D non-rigid object retrieval |
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https://doi.org/10.1007/s10489-017-1114-x |
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Belaid, Samir Naanaa, Wady Ben Romdhane, Lotfi |
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10.1007/s10489-017-1114-x |
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