Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the...
Saved in:
| Published in | IEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 6; pp. 1005 - 1018 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Alamitos, CA
IEEE
01.06.2007
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0162-8828 2160-9292 1939-3539 |
| DOI | 10.1109/TPAMI.2007.1037 |
Cover
| Abstract | We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency |
|---|---|
| AbstractList | We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. Canonical Correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical Linear Discriminant Analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency. We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. Canonical Correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical Linear Discriminant Analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency.We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. Canonical Correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical Linear Discriminant Analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency. We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal [abstract truncated by publisher]. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. |
| Author | Kittler, J. Tae-Kyun Kim Cipolla, R. |
| Author_xml | – sequence: 1 surname: Tae-Kyun Kim fullname: Tae-Kyun Kim organization: Dept. of Eng., Cambridge Univ – sequence: 2 givenname: J. surname: Kittler fullname: Kittler, J. – sequence: 3 givenname: R. surname: Cipolla fullname: Cipolla, R. |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18734283$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/17431299$$D View this record in MEDLINE/PubMed |
| BookMark | eNqF0ktr3DAUBWBREppJ2nUXhWIKaVaeXL1saRncRwamtLTJoisjy9eDgkdOJE-h_z7yzDSBLJKVN9_RlXXPMTnwg0dC3lGYUwr6_OrnxffFnAGUcwq8fEVmjBaQa6bZAZkBLViuFFNH5DjGGwAqJPDX5IiWglOm9Yz8-eyiDW7tvBndX8yWaIJ3fpUZ32a_0A4r70Y3-GzossXarDD7jWNW9SZGjNl1nGhl0q2cNX1WDSFgb6ZAfEMOO9NHfLv_npDrr1-uqst8-ePborpY5lZyPuYowArdKWSiLQ1rpNCGWsV1y1qlWyhUw0AyoZFqa1grGyiMZUpKq2zXUH5Cznbn3obhboNxrNfpl7DvjcdhE2sNvKBCKf2iVAoKqSRVSX56VpbAleBQvAi5EJQWmif48Qm8GTbBp4epVTEZvh37YY82zRrb-jYtxoR_9f91JXC6Byam9-6C8dbFR6dKLpiapp3vnA1DjAG7RwL1VJx6W5x6Kk49FScl5JOEdeN2kWMwrn8m936Xc4j4MEWkEmpJ-T0nRMzZ |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1016_j_imavis_2016_03_010 crossref_primary_10_1016_j_knosys_2022_109667 crossref_primary_10_1109_LSP_2008_2010819 crossref_primary_10_1016_j_ins_2012_09_008 crossref_primary_10_1016_j_jvcir_2014_08_006 crossref_primary_10_1109_TIP_2019_2954176 crossref_primary_10_1007_s13042_019_00941_6 crossref_primary_10_1016_j_neucom_2019_06_066 crossref_primary_10_1016_j_sigpro_2018_10_018 crossref_primary_10_1109_TCSVT_2011_2181452 crossref_primary_10_1109_TCSVT_2016_2538520 crossref_primary_10_1109_TPAMI_2015_2408358 crossref_primary_10_1016_j_procs_2015_07_316 crossref_primary_10_1016_j_patcog_2017_01_035 crossref_primary_10_1109_ACCESS_2018_2794357 crossref_primary_10_1109_TIP_2019_2913511 crossref_primary_10_1109_TIP_2011_2169972 crossref_primary_10_1109_TIP_2016_2616297 crossref_primary_10_1109_LSP_2014_2319817 crossref_primary_10_1007_s11760_017_1198_0 crossref_primary_10_1007_s00138_016_0772_8 crossref_primary_10_1109_TPAMI_2017_2776154 crossref_primary_10_1016_j_patrec_2017_09_026 crossref_primary_10_1016_j_jvcir_2014_01_009 crossref_primary_10_1016_j_neucom_2016_04_067 crossref_primary_10_1016_j_neucom_2023_01_084 crossref_primary_10_1016_j_mlwa_2022_100407 crossref_primary_10_1007_s11042_023_14433_x crossref_primary_10_1631_jzus_C1200032 crossref_primary_10_1016_j_imavis_2011_08_002 crossref_primary_10_1016_j_patcog_2016_10_004 crossref_primary_10_1587_transinf_2014EDP7057 crossref_primary_10_1109_TPAMI_2014_2339851 crossref_primary_10_1016_j_neucom_2007_11_006 crossref_primary_10_1016_j_patcog_2021_108190 crossref_primary_10_1109_TPAMI_2008_167 crossref_primary_10_1155_2011_790598 crossref_primary_10_1109_TIP_2017_2746993 crossref_primary_10_1016_j_cviu_2017_03_004 crossref_primary_10_1016_j_neucom_2022_10_040 crossref_primary_10_3390_app13074209 crossref_primary_10_1109_TII_2022_3148289 crossref_primary_10_1016_j_neucom_2013_01_012 crossref_primary_10_1016_j_neucom_2016_01_051 crossref_primary_10_1016_j_patrec_2015_09_008 crossref_primary_10_1109_TIP_2017_2765820 crossref_primary_10_1109_TKDE_2019_2958342 crossref_primary_10_1080_07038992_2015_1089161 crossref_primary_10_1016_j_knosys_2021_107799 crossref_primary_10_1016_j_engappai_2019_03_016 crossref_primary_10_1007_s11042_015_3090_7 crossref_primary_10_1007_s10994_013_5380_5 crossref_primary_10_1016_j_amc_2017_07_058 crossref_primary_10_1109_TMM_2022_3173535 crossref_primary_10_1109_TSP_2012_2203816 crossref_primary_10_1016_j_neucom_2024_127523 crossref_primary_10_1109_TIFS_2016_2569061 crossref_primary_10_1016_j_knosys_2023_110590 crossref_primary_10_1016_j_imavis_2016_07_003 crossref_primary_10_1016_j_patrec_2018_11_009 crossref_primary_10_1109_TMM_2008_921847 crossref_primary_10_1109_TPAMI_2024_3466315 crossref_primary_10_1007_s11042_019_08408_0 crossref_primary_10_1016_j_jvcir_2018_05_016 crossref_primary_10_1016_j_chaos_2015_11_038 crossref_primary_10_1109_ACCESS_2019_2947548 crossref_primary_10_1016_j_imavis_2016_07_008 crossref_primary_10_3390_s19225051 crossref_primary_10_3233_THC_174534 crossref_primary_10_1007_s13042_021_01336_2 crossref_primary_10_1016_j_patrec_2009_06_002 crossref_primary_10_1016_j_asoc_2018_08_010 crossref_primary_10_1109_TNNLS_2018_2844866 crossref_primary_10_1016_j_neucom_2014_06_015 crossref_primary_10_1109_TPAMI_2011_283 crossref_primary_10_1371_journal_pone_0176598 crossref_primary_10_1109_TIP_2023_3240863 crossref_primary_10_1016_j_patcog_2014_05_011 crossref_primary_10_1145_2037661_2037666 crossref_primary_10_3390_math12142164 crossref_primary_10_1007_s11263_018_1088_0 crossref_primary_10_1016_j_neucom_2016_12_004 crossref_primary_10_1631_FITEE_1601764 crossref_primary_10_1109_TNNLS_2020_2980059 crossref_primary_10_1007_s11263_017_1000_3 crossref_primary_10_1109_TIP_2007_914203 crossref_primary_10_1109_TPAMI_2011_52 crossref_primary_10_1016_j_jvcir_2014_09_004 crossref_primary_10_1109_TIT_2022_3207686 crossref_primary_10_1016_j_cam_2022_114953 crossref_primary_10_1109_TPAMI_2008_154 crossref_primary_10_1080_2150704X_2015_1062156 crossref_primary_10_1016_j_neucom_2019_12_026 crossref_primary_10_1109_TKDE_2018_2872063 crossref_primary_10_1109_LSP_2016_2577601 crossref_primary_10_1016_j_neucom_2015_10_004 crossref_primary_10_1016_j_patcog_2017_11_020 crossref_primary_10_1016_j_eswa_2023_120558 crossref_primary_10_1016_j_ins_2017_01_001 crossref_primary_10_1016_j_eswa_2019_06_062 crossref_primary_10_1007_s11063_020_10276_x crossref_primary_10_1109_TCSVT_2017_2772026 crossref_primary_10_1109_TPAMI_2015_2505311 crossref_primary_10_1016_j_eswa_2019_05_025 crossref_primary_10_1049_htl_2018_5024 crossref_primary_10_1016_j_eswa_2020_114559 crossref_primary_10_1137_130919222 crossref_primary_10_1016_j_cviu_2017_04_008 crossref_primary_10_1109_TIP_2017_2745106 crossref_primary_10_1109_TIFS_2014_2324277 crossref_primary_10_1109_TIP_2015_2466106 crossref_primary_10_1016_j_jvcir_2013_02_002 crossref_primary_10_1016_j_imavis_2016_06_005 crossref_primary_10_1007_s00521_019_04419_y crossref_primary_10_1007_s10851_015_0629_1 crossref_primary_10_1016_j_neucom_2014_07_049 crossref_primary_10_1109_MIS_2016_26 crossref_primary_10_1007_s11063_014_9358_5 crossref_primary_10_1016_j_dsp_2020_102809 crossref_primary_10_1142_S0218001408006685 crossref_primary_10_1016_j_jvcir_2019_102660 crossref_primary_10_1109_TMM_2012_2228476 crossref_primary_10_1016_j_neucom_2016_07_075 crossref_primary_10_1007_s11042_015_3070_y crossref_primary_10_1016_j_patcog_2016_09_043 crossref_primary_10_3390_app10217827 crossref_primary_10_1109_TCBB_2016_2603987 crossref_primary_10_1016_j_ins_2019_12_041 crossref_primary_10_1142_S2737480724410061 crossref_primary_10_1007_s00521_023_09159_8 crossref_primary_10_1016_j_patcog_2010_07_012 crossref_primary_10_1109_LGRS_2017_2765341 crossref_primary_10_1080_13682199_2023_2225372 crossref_primary_10_1007_s10489_020_01730_3 crossref_primary_10_1109_TNNLS_2022_3212703 crossref_primary_10_1007_s11263_010_0381_3 crossref_primary_10_1016_j_patcog_2010_02_007 crossref_primary_10_1109_ACCESS_2015_2485400 crossref_primary_10_1109_TIP_2015_2393057 crossref_primary_10_1016_j_micpro_2020_103096 crossref_primary_10_1186_1687_6180_2014_15 crossref_primary_10_1016_j_neucom_2016_05_013 crossref_primary_10_1016_j_patcog_2017_02_005 crossref_primary_10_1016_j_patcog_2017_02_007 crossref_primary_10_1016_j_sigpro_2014_08_046 crossref_primary_10_1109_JSTARS_2015_2513481 crossref_primary_10_1109_JPROC_2020_2989782 crossref_primary_10_1109_TIP_2023_3261758 crossref_primary_10_1016_j_asoc_2019_105630 crossref_primary_10_1016_j_patrec_2015_03_008 crossref_primary_10_1109_TIP_2018_2882225 crossref_primary_10_1016_j_neucom_2016_01_113 crossref_primary_10_1007_s13042_017_0782_5 crossref_primary_10_1016_j_neucom_2015_07_027 crossref_primary_10_1016_j_neucom_2011_11_006 crossref_primary_10_1016_j_neucom_2017_05_099 crossref_primary_10_1109_TIFS_2009_2026455 crossref_primary_10_1016_j_eswa_2022_119062 crossref_primary_10_1080_1931308X_2013_799313 crossref_primary_10_1016_j_inffus_2017_09_001 crossref_primary_10_1007_s40314_021_01482_x crossref_primary_10_1016_j_neucom_2016_01_126 crossref_primary_10_4018_IJDCF_2016100103 crossref_primary_10_1109_TKDE_2012_92 crossref_primary_10_1016_j_neucom_2019_03_010 crossref_primary_10_1109_JSEN_2020_3004581 crossref_primary_10_1016_j_jocs_2016_10_016 crossref_primary_10_1109_TNNLS_2020_2978508 crossref_primary_10_1016_j_jvcir_2015_12_001 crossref_primary_10_1109_TIP_2015_2493448 crossref_primary_10_1016_j_cviu_2008_02_003 crossref_primary_10_1109_TNNLS_2023_3276796 crossref_primary_10_1007_s11063_019_10133_6 crossref_primary_10_1016_j_jvcir_2018_02_004 crossref_primary_10_1109_JSTARS_2018_2868142 crossref_primary_10_4018_IJISMD_313577 crossref_primary_10_1109_TPAMI_2008_200 crossref_primary_10_1016_j_neucom_2016_08_002 crossref_primary_10_1109_TBDATA_2024_3366084 crossref_primary_10_1016_j_eswa_2019_112886 crossref_primary_10_1109_TNNLS_2020_3044176 crossref_primary_10_1145_3708498 crossref_primary_10_1109_TIM_2021_3134333 crossref_primary_10_1109_TPAMI_2014_2353635 crossref_primary_10_5772_54002 crossref_primary_10_1016_j_patcog_2020_107500 crossref_primary_10_1109_ACCESS_2017_2733718 crossref_primary_10_1016_j_patcog_2013_10_012 crossref_primary_10_3233_JIFS_181347 crossref_primary_10_1109_TPAMI_2015_2414422 crossref_primary_10_1016_j_patcog_2015_03_016 crossref_primary_10_1109_TCSVT_2014_2351094 crossref_primary_10_1007_s00530_019_00629_5 crossref_primary_10_1109_TIP_2022_3219235 crossref_primary_10_1016_j_neucom_2013_12_015 crossref_primary_10_1007_s11263_015_0833_x crossref_primary_10_1016_j_bspc_2021_102780 crossref_primary_10_1016_j_patcog_2015_03_011 crossref_primary_10_1109_TIP_2022_3212284 crossref_primary_10_1109_TIFS_2016_2601060 crossref_primary_10_1016_j_patcog_2020_107754 crossref_primary_10_4018_JGIM_2017100107 crossref_primary_10_1007_s11263_014_0720_x crossref_primary_10_1109_TIP_2013_2282996 crossref_primary_10_1109_JSTSP_2015_2419593 crossref_primary_10_1007_s00521_016_2758_x crossref_primary_10_1007_s11042_017_5076_0 crossref_primary_10_1109_TCSVT_2015_2412831 crossref_primary_10_1007_s11042_018_6045_y crossref_primary_10_1109_TIP_2016_2577885 crossref_primary_10_1109_TNNLS_2015_2504724 crossref_primary_10_1016_j_patcog_2019_107028 crossref_primary_10_1007_s00521_018_3367_7 crossref_primary_10_1049_ipr2_12131 crossref_primary_10_1109_TSMCB_2011_2169056 crossref_primary_10_1109_TCSVT_2013_2280098 crossref_primary_10_1016_j_padiff_2024_100820 crossref_primary_10_1016_j_patcog_2010_11_004 crossref_primary_10_1016_j_knosys_2018_10_043 crossref_primary_10_1109_TIP_2024_3419414 crossref_primary_10_1016_j_neucom_2013_12_004 crossref_primary_10_1016_j_patcog_2017_03_001 crossref_primary_10_1016_j_neucom_2014_10_113 crossref_primary_10_1109_TNNLS_2013_2288062 crossref_primary_10_1016_j_bspc_2018_08_010 crossref_primary_10_1186_s13640_020_00507_5 crossref_primary_10_1007_s10515_017_0220_7 crossref_primary_10_1016_j_neucom_2024_127372 crossref_primary_10_1016_j_ins_2017_08_004 crossref_primary_10_1109_TIP_2022_3193758 crossref_primary_10_1109_TIP_2009_2038621 crossref_primary_10_1109_TCYB_2020_2984489 crossref_primary_10_1016_j_patrec_2020_10_015 crossref_primary_10_1142_S0218213013500309 crossref_primary_10_1007_s11767_011_0535_7 crossref_primary_10_1007_s11432_019_3019_3 crossref_primary_10_1109_TIP_2015_2463223 crossref_primary_10_1109_TIP_2018_2866688 crossref_primary_10_1109_TKDE_2009_126 crossref_primary_10_1016_j_imavis_2016_04_003 crossref_primary_10_1016_j_knosys_2015_01_016 crossref_primary_10_1587_transinf_E97_D_1855 crossref_primary_10_1109_TIP_2012_2206039 crossref_primary_10_1109_TMM_2015_2510509 crossref_primary_10_1109_TIP_2015_2445293 crossref_primary_10_1007_s00521_020_05089_x crossref_primary_10_1109_TIP_2020_3040847 crossref_primary_10_1016_j_ins_2018_02_062 crossref_primary_10_1016_j_eswa_2021_114916 crossref_primary_10_4018_IJIIT_2018100105 crossref_primary_10_1016_j_neucom_2018_09_090 crossref_primary_10_1016_j_patrec_2013_07_004 crossref_primary_10_1016_j_patrec_2020_05_018 crossref_primary_10_1016_j_patcog_2013_09_009 crossref_primary_10_1016_j_neucom_2018_04_073 crossref_primary_10_1016_j_patcog_2019_107123 crossref_primary_10_1109_THMS_2017_2681425 crossref_primary_10_1109_TCSVT_2022_3206865 crossref_primary_10_1007_s11042_018_6071_9 crossref_primary_10_1109_TIFS_2010_2077627 crossref_primary_10_1109_TMM_2012_2234731 crossref_primary_10_1016_j_patcog_2011_02_011 crossref_primary_10_1007_s11042_018_6578_0 crossref_primary_10_1016_j_patrec_2017_04_005 crossref_primary_10_1109_TCSVT_2015_2469571 crossref_primary_10_1007_s11042_020_08989_1 crossref_primary_10_1109_TCSVT_2014_2309834 crossref_primary_10_1007_s11042_022_12435_9 crossref_primary_10_1016_j_compchemeng_2018_06_017 crossref_primary_10_1109_ACCESS_2018_2841855 crossref_primary_10_1016_j_image_2011_05_002 crossref_primary_10_1109_TIP_2017_2713940 crossref_primary_10_1109_TCSVT_2014_2367357 crossref_primary_10_1109_TIP_2015_2426413 crossref_primary_10_1016_j_ins_2021_08_093 crossref_primary_10_1016_j_jvcir_2021_103045 crossref_primary_10_1109_TMM_2018_2866222 crossref_primary_10_1007_s11042_018_6006_5 |
| Cites_doi | 10.1109/CVPR.2005.151 10.1109/AFGR.1998.670971 10.1007/11527923_8 10.1109/cvpr.2003.1211369 10.1109/TPAMI.2005.58 10.21236/ada415962 10.1023/B:VISI.0000013087.49260.fb 10.1007/11744078_20 10.1109/AFGR.1998.670968 10.1109/AFGR.2004.1301539 10.1109/TIT.1974.1055174 10.1007/3-540-47977-5_56 10.1007/11008941_21 10.1090/S0025-5718-1973-0348991-3 10.5244/C.14.25 10.1007/978-3-642-69878-1 10.1162/0899766042321814 10.5244/C.19.58 10.6028/NIST.IR.6965 10.1109/34.598228 10.1002/0471200611 10.1007/11612704_32 10.1016/S1077-3142(03)00080-8 10.1109/CVPR.2003.1211497 10.2307/2333955 10.1038/44565 10.1016/S0167-8655(03)00117-X 10.1109/AFGR.2004.1301634 10.1023/B:VISI.0000042993.50813.60 10.1109/CVPR.2003.1211373 10.1109/AFGR.2002.4527207 10.1109/AFGR.2000.840629 |
| ContentType | Journal Article |
| Copyright | 2007 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007 |
| Copyright_xml | – notice: 2007 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007 |
| DBID | 97E RIA RIE AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3 7X8 |
| DOI | 10.1109/TPAMI.2007.1037 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering Engineering Research Database MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional Engineering Research Database ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
| DatabaseTitleList | Technology Research Database MEDLINE MEDLINE - Academic Technology Research Database Technology Research Database Technology Research Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Applied Sciences |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 1018 |
| ExternalDocumentID | 2333971531 17431299 18734283 10_1109_TPAMI_2007_1037 4160951 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E 9M8 AAJGR AARMG AASAJ AAWTH ABAZT ABFSI ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ADRHT AENEX AETEA AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P FA8 HZ~ H~9 IBMZZ ICLAB IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNI RNS RXW RZB TAE TN5 UHB VH1 XJT ~02 AAYXX CITATION IQODW RIG AAYOK CGR CUY CVF ECM EIF NPM PKN RIC Z5M 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3 7X8 |
| ID | FETCH-LOGICAL-c533t-e40c49f8e24d7a2b549a1c839d2d89d068b205249e19ca2d5b06ac2855c8cfb13 |
| IEDL.DBID | RIE |
| ISSN | 0162-8828 |
| IngestDate | Thu Oct 02 04:07:11 EDT 2025 Sat Sep 27 19:04:43 EDT 2025 Wed Oct 01 13:52:41 EDT 2025 Thu Oct 02 11:52:05 EDT 2025 Mon Jun 30 06:15:39 EDT 2025 Wed Feb 19 01:43:08 EST 2025 Mon Jul 21 09:14:20 EDT 2025 Wed Oct 01 06:41:36 EDT 2025 Thu Apr 24 23:03:49 EDT 2025 Tue Aug 26 16:42:03 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Image recognition Image processing Canonical correlation Discriminant function Correlation method Parametric method Canonical analysis Image matching Vector space Lighting Classification Facies principal angles Database Illumination orthogonal subspace method Robustness linear discriminant analysis Pattern analysis Computer vision Discriminant analysis Motion estimation Face recognition Subspace method Pattern recognition canonical correlation analysis Object recognition Luminance Correlation analysis Artificial intelligence image sets |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c533t-e40c49f8e24d7a2b549a1c839d2d89d068b205249e19ca2d5b06ac2855c8cfb13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
| PMID | 17431299 |
| PQID | 864116318 |
| PQPubID | 23500 |
| PageCount | 14 |
| ParticipantIDs | pascalfrancis_primary_18734283 crossref_primary_10_1109_TPAMI_2007_1037 pubmed_primary_17431299 proquest_miscellaneous_34411693 proquest_miscellaneous_903614889 proquest_journals_864116318 proquest_miscellaneous_880658518 ieee_primary_4160951 proquest_miscellaneous_70384306 crossref_citationtrail_10_1109_TPAMI_2007_1037 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2007-06-01 |
| PublicationDateYYYYMMDD | 2007-06-01 |
| PublicationDate_xml | – month: 06 year: 2007 text: 2007-06-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | Los Alamitos, CA |
| PublicationPlace_xml | – name: Los Alamitos, CA – name: United States – name: New York |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2007 |
| Publisher | IEEE IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: IEEE Computer Society – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref35 ref12 ref34 ref37 ref36 ref31 ref11 ref33 ref10 Kozakaya (ref27) 2004; 45 ref2 ref1 ref17 ref39 ref38 ref19 ref18 (ref40) 2005 Gittins (ref5) 1985 ref24 ref26 ref25 ref20 ref22 Phillips (ref16) 2003 ref21 Wang (ref30) Via (ref32) Oja (ref4) 1983 ref28 ref29 ref8 ref7 ref9 ref3 Lee (ref15) 2001 ref6 Duda (ref14) 2000 Wolf (ref23) 2003; 4 |
| References_xml | – ident: ref34 doi: 10.1109/CVPR.2005.151 – start-page: 556 year: 2001 ident: ref15 article-title: Algorithms for Non-Negative Matirx Factorization publication-title: Advances in Neural Information Processing Systems – ident: ref9 doi: 10.1109/AFGR.1998.670971 – volume-title: Proc. 13th European Signal Processing Conf. ident: ref32 article-title: Canonical Correlation Analysis (CCA) Algorithms for Multiple Data Sets: Application to Blind SIMO Equalization – ident: ref37 doi: 10.1007/11527923_8 – ident: ref20 doi: 10.1109/cvpr.2003.1211369 – ident: ref35 doi: 10.1109/TPAMI.2005.58 – ident: ref13 doi: 10.21236/ada415962 – ident: ref31 doi: 10.1023/B:VISI.0000013087.49260.fb – volume: 45 start-page: 951 issue: 3 year: 2004 ident: ref27 article-title: Development and Evaluation of Face Recognition System Using Constrained Mutual Subspace Method publication-title: IPSJ J. – ident: ref38 doi: 10.1007/11744078_20 – ident: ref8 doi: 10.1109/AFGR.1998.670968 – volume-title: Pattern Classification year: 2000 ident: ref14 – ident: ref29 doi: 10.1109/AFGR.2004.1301539 – start-page: 259 volume-title: Proc. Computer Vision and Pattern Recognition Conf. ident: ref30 article-title: Random Sampling LDA for Face Recognition – ident: ref3 doi: 10.1109/TIT.1974.1055174 – ident: ref17 doi: 10.1007/3-540-47977-5_56 – ident: ref24 doi: 10.1007/11008941_21 – ident: ref2 doi: 10.1090/S0025-5718-1973-0348991-3 – ident: ref11 doi: 10.5244/C.14.25 – volume-title: Canonical Analysis: A Review with Applications in Ecology year: 1985 ident: ref5 doi: 10.1007/978-3-642-69878-1 – ident: ref26 doi: 10.1162/0899766042321814 – ident: ref36 doi: 10.5244/C.19.58 – volume-title: Facial Recognition Vendor Test 2002: Evaluation Report year: 2003 ident: ref16 doi: 10.6028/NIST.IR.6965 – ident: ref7 doi: 10.1109/34.598228 – ident: ref6 doi: 10.1002/0471200611 – ident: ref39 doi: 10.1007/11612704_32 – ident: ref21 doi: 10.1016/S1077-3142(03)00080-8 – ident: ref25 doi: 10.1109/CVPR.2003.1211497 – ident: ref1 doi: 10.2307/2333955 – ident: ref10 doi: 10.1038/44565 – ident: ref19 doi: 10.1016/S0167-8655(03)00117-X – year: 2005 ident: ref40 article-title: Facepass – volume: 4 start-page: 913 issue: 10 year: 2003 ident: ref23 article-title: Learning over Sets Using Kernel Principal Angles publication-title: J. Machine Learning Research – ident: ref28 doi: 10.1109/AFGR.2004.1301634 – ident: ref33 doi: 10.1023/B:VISI.0000042993.50813.60 – volume-title: Subspace Methods of Pattern Recognition year: 1983 ident: ref4 – ident: ref22 doi: 10.1109/CVPR.2003.1211373 – ident: ref18 doi: 10.1109/AFGR.2002.4527207 – ident: ref12 doi: 10.1109/AFGR.2000.840629 |
| SSID | ssj0014503 |
| Score | 2.4642856 |
| Snippet | We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing... Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1005 |
| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Cameras canonical correlation canonical correlation analysis Cluster Analysis Computer science; control theory; systems Computer vision Correlation Correlation analysis Discriminant Analysis Exact sciences and technology Face recognition Illumination Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image recognition image sets Information Storage and Retrieval - methods Learning Lighting Linear discriminant analysis Matching Methods Neural networks Numerical Analysis, Computer-Assisted Object recognition orthogonal subspace method Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry principal angles Recognition Reproducibility of Results Robustness Sensitivity and Specificity Signal Processing, Computer-Assisted Statistics as Topic Studies Subtraction Technique Vectors |
| Title | Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations |
| URI | https://ieeexplore.ieee.org/document/4160951 https://www.ncbi.nlm.nih.gov/pubmed/17431299 https://www.proquest.com/docview/864116318 https://www.proquest.com/docview/34411693 https://www.proquest.com/docview/70384306 https://www.proquest.com/docview/880658518 https://www.proquest.com/docview/903614889 |
| Volume | 29 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB61PcGBQssjFIoPHDiQrZM4iX2sFqoWaRGCViqnyHZshICk6mYv_HpmnMcCIhK3SJk8nJmxv4lnvgF4STGAyzSPraJfN0LpWFruY8O9t14bUXuqRl69L86vxLvr_HoHXk-1MM65kHzmFnQY9vLr1m7oV9kJggdCBLuwW8qir9WadgxEHrogI4JBD8cwYqDxSbg6ufxwurro2QqpKo54QmndTAPf63YxCt1VKDdSr_Hz-L6vxTzwDAvQ2T6sxlfv806-LTadWdiff7E6_u_Y7sO9AYmy0950HsCOaw5gf-zywAanP4C7v1EWHsLnN19poqEEGpoo2cDP-oXppmYfx2yktmGtZxc_cLJin1zHQutNt2YhQYEtddOGcky2pN4gQzbeQ7g6e3u5PI-H9gyxRYzYxU5wK5SXLhV1qVODkaZOLAKuOq2lqnkhTcpzDO9coqxO69zwQttU5rmV1pskewR7-Dj3BJi2IiltoUvrvJC6MFq7uii1Q2PR3ogIFqOeKjtwl1MLje9ViGG4qoKOqadmWZGOI3g1XXDT03bMix6SNiaxQRERHP9hCNvbyDIjlroIjkbLqAa_X1eyEAki3ERG8GI6iw5LuzC6ce1mXWWCRFQ2L4GTsBQYykXAZiQkbYcjVpbzIgqhCYa6UkXwuDfb7QgG63_675EfwZ0xKZInz2Cvu92454i8OnMcXO4XpC0qlg |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Nb9QwEB2VcoAeKLRQQqH1gQMHsnUSJ7GP1UK1C90KwVYqp8h2bISABLHZC78ej-NkAbESt0iZfDgzY7-JZ94APMcYwGSSxlrgrxsmZMw1tbGi1morFastViMvrorZNXtzk9_swMuxFsYY45PPzAQP_V5-3eo1_io7c-ABEcEtuJ0zxvK-WmvcM2C574PsMIzzcRdIBCKfhIqz5bvzxbznK8S6OGQKxZUz9Yyvm-XI91fB7Ei5ch_I9p0ttkNPvwRd7MNiePk-8-TLZN2pif75F6_j_47uPtwLWJSc98bzAHZMcwD7Q58HEtz-APZ-Iy08hI-vPuNUgyk0OFWSwND6icimJu-HfKS2Ia0l829uuiIfTEd8802zIj5FgUxl0_qCTDLF7iAhH-8hXF-8Xk5ncWjQEGuHErvYMKqZsNykrC5lqlysKRPtIFed1lzUtOAqpbkL8EwitEzrXNFC6pTnuebaqiR7BLvuceYxEKlZUupCltpYxmWhpDR1UUrjzEVaxSKYDHqqdGAvxyYaXysfxVBReR1jV82yQh1H8GK84HtP3LFd9BC1MYoFRURw8ochbG7Dywx56iI4HiyjCp6_qnjBEodxEx7B6XjWuSzuw8jGtOtVlTEUEdl2CTcNc-aCuQjIFgmOG-IOLfPtIsKBExfschHBUW-2mxEE63_y75Gfwp3ZcnFZXc6v3h7D3SFFkiZPYbf7sTbPHA7r1Il3v19b5S3j |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Discriminative+learning+and+recognition+of+image+set+classes+using+canonical+correlations&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Kim%2C+Tae-Kyun&rft.au=Kittler%2C+Josef&rft.au=Cipolla%2C+Roberto&rft.date=2007-06-01&rft.issn=0162-8828&rft.volume=29&rft.issue=6&rft.spage=1005&rft_id=info:doi/10.1109%2FTPAMI.2007.1037&rft_id=info%3Apmid%2F17431299&rft.externalDocID=17431299 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |