Regularized constraint subspace based method for image set classification
Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discar...
Ausführliche Beschreibung
Autor*in: |
Tan, Hengliang [verfasserIn] Gao, Ying [verfasserIn] Ma, Zhengming [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 76 |
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Übergeordnetes Werk: |
volume:76 |
DOI / URN: |
10.1016/j.patcog.2017.11.020 |
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Katalog-ID: |
ELV000668710 |
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245 | 1 | 0 | |a Regularized constraint subspace based method for image set classification |
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520 | |a Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. | ||
650 | 4 | |a Subspace method | |
650 | 4 | |a Constraint subspace | |
650 | 4 | |a Difference subspace | |
650 | 4 | |a Orthogonal subspace | |
650 | 4 | |a Eigenspectrum regularization model | |
700 | 1 | |a Gao, Ying |e verfasserin |4 aut | |
700 | 1 | |a Ma, Zhengming |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Pattern recognition |d Amsterdam : Elsevier, 1968 |g 76 |h Online-Ressource |w (DE-627)265784131 |w (DE-600)1466343-0 |w (DE-576)101177364 |7 nnns |
773 | 1 | 8 | |g volume:76 |
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10.1016/j.patcog.2017.11.020 doi (DE-627)ELV000668710 (ELSEVIER)S0031-3203(17)30474-0 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Tan, Hengliang verfasserin aut Regularized constraint subspace based method for image set classification 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model Gao, Ying verfasserin aut Ma, Zhengming verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 76 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:76 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 76 |
spelling |
10.1016/j.patcog.2017.11.020 doi (DE-627)ELV000668710 (ELSEVIER)S0031-3203(17)30474-0 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Tan, Hengliang verfasserin aut Regularized constraint subspace based method for image set classification 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model Gao, Ying verfasserin aut Ma, Zhengming verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 76 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:76 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 76 |
allfields_unstemmed |
10.1016/j.patcog.2017.11.020 doi (DE-627)ELV000668710 (ELSEVIER)S0031-3203(17)30474-0 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Tan, Hengliang verfasserin aut Regularized constraint subspace based method for image set classification 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model Gao, Ying verfasserin aut Ma, Zhengming verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 76 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:76 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 76 |
allfieldsGer |
10.1016/j.patcog.2017.11.020 doi (DE-627)ELV000668710 (ELSEVIER)S0031-3203(17)30474-0 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Tan, Hengliang verfasserin aut Regularized constraint subspace based method for image set classification 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model Gao, Ying verfasserin aut Ma, Zhengming verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 76 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:76 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 76 |
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10.1016/j.patcog.2017.11.020 doi (DE-627)ELV000668710 (ELSEVIER)S0031-3203(17)30474-0 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Tan, Hengliang verfasserin aut Regularized constraint subspace based method for image set classification 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model Gao, Ying verfasserin aut Ma, Zhengming verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 76 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:76 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 76 |
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000 150 DE-600 54.74 bkl Regularized constraint subspace based method for image set classification Subspace method Constraint subspace Difference subspace Orthogonal subspace Eigenspectrum regularization model |
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ddc 000 bkl 54.74 misc Subspace method misc Constraint subspace misc Difference subspace misc Orthogonal subspace misc Eigenspectrum regularization model |
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Regularized constraint subspace based method for image set classification |
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Regularized constraint subspace based method for image set classification |
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regularized constraint subspace based method for image set classification |
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Regularized constraint subspace based method for image set classification |
abstract |
Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. |
abstractGer |
Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. |
abstract_unstemmed |
Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches. |
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Regularized constraint subspace based method for image set classification |
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