Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications
Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a L...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence Jg. 35; H. 1; S. 92 - 104 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Los Alamitos, CA
IEEE
01.01.2013
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation. |
|---|---|
| AbstractList | Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation. Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation.Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the locality and the similarity among the instances to be encoded are lost. To preserve such locality and similarity information, we propose a Laplacian sparse coding (LSc) framework. By incorporating the similarity preserving term into the objective of sparse coding, our proposed Laplacian sparse coding can alleviate the instability of sparse codes. Furthermore, we propose a Hypergraph Laplacian sparse coding (HLSc), which extends our Laplacian sparse coding to the case where the similarity among the instances defined by a hypergraph. Specifically, this HLSc captures the similarity among the instances within the same hyperedge simultaneously, and also makes the sparse codes of them be similar to each other. Both Laplacian sparse coding and Hypergraph Laplacian sparse coding enhance the robustness of sparse coding. We apply the Laplacian sparse coding to feature quantization in Bag-of-Words image representation, and it outperforms sparse coding and achieves good performance in solving the image classification problem. The Hypergraph Laplacian sparse coding is also successfully used to solve the semi-auto image tagging problem. The good performance of these applications demonstrates the effectiveness of our proposed formulations in locality and similarity preservation. |
| Author | Liang-Tien Chia Shenghua Gao Tsang, Ivor Wai-Hung |
| Author_xml | – sequence: 1 surname: Shenghua Gao fullname: Shenghua Gao email: gaos0004@ntu.edu.sg organization: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 2 givenname: Ivor Wai-Hung surname: Tsang fullname: Tsang, Ivor Wai-Hung email: ivortsang@ntu.edu.sg organization: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore – sequence: 3 surname: Liang-Tien Chia fullname: Liang-Tien Chia email: asltchia@ntu.edu.sg organization: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27677736$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/22392702$$D View this record in MEDLINE/PubMed |
| BookMark | eNqF0U1r3DAQBmBRUpJNmmNPhWAogR7qjUaSZfu4LPmCDS00OYvRh1MFr-xI3kP-fZXspoVA6Gkuz7ww8x6SvTAER8hnoHMA2p7d_lzcXM8ZBTaX_AOZMZC0bFnL9siMgmRl07DmgBym9EApiIryfXLAGG9ZTdmM3Kxw7NF4DMWvEWNyxXKwPtx_L66eRhfvI46_i3cNBlssxrH3Bic_hPSJfOywT-54N4_I3cX57fKqXP24vF4uVqURNZ1Kq51wHRct18y62nIm88BOQGM4F7apdEWtlNhqITrhXGO1RkAwnW4r3fAj8m2bO8bhcePSpNY-Gdf3GNywSQo4VJDzBfs_ZVLWABIg069v6MOwiSEfkhUVsmJUtlmd7NRGr51VY_RrjE_q9acZnO4AJoN9FzEYn_65WtZ1zWV2fOtMHFKKrlPGTy9_nCL6XgFVzw2rl4bVc8NK8rxVvtl6DX7Pf9l675z7ayXIiueT_wBL5K2y |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1109_TNNLS_2018_2856096 crossref_primary_10_1109_LGRS_2013_2290531 crossref_primary_10_1109_TIP_2014_2353814 crossref_primary_10_1109_TSIPN_2016_2631890 crossref_primary_10_1080_01431161_2016_1277042 crossref_primary_10_1016_j_patcog_2015_12_005 crossref_primary_10_1016_j_ins_2017_12_025 crossref_primary_10_1016_j_ins_2015_07_005 crossref_primary_10_1016_j_ins_2017_12_020 crossref_primary_10_1016_j_neucom_2021_12_063 crossref_primary_10_1016_j_cviu_2016_10_012 crossref_primary_10_1109_TIP_2019_2934576 crossref_primary_10_1109_JSTARS_2016_2602305 crossref_primary_10_1109_TCSVT_2017_2678443 crossref_primary_10_1016_j_neucom_2015_03_086 crossref_primary_10_1002_wics_1646 crossref_primary_10_1109_TGRS_2015_2399978 crossref_primary_10_1109_TSP_2016_2550016 crossref_primary_10_1109_TIE_2014_2378735 crossref_primary_10_1109_TMM_2013_2284755 crossref_primary_10_1016_j_future_2021_09_044 crossref_primary_10_1016_j_neucom_2015_10_048 crossref_primary_10_1109_TPAMI_2022_3168882 crossref_primary_10_1109_ACCESS_2023_3331592 crossref_primary_10_1109_TCSVT_2019_2963862 crossref_primary_10_1016_j_patcog_2017_04_030 crossref_primary_10_1109_ACCESS_2017_2730281 crossref_primary_10_1109_TIP_2015_2465171 crossref_primary_10_1016_j_neucom_2015_07_084 crossref_primary_10_1109_TGRS_2017_2761893 crossref_primary_10_1186_s13640_017_0178_1 crossref_primary_10_3390_rs8120985 crossref_primary_10_1109_TIP_2016_2526910 crossref_primary_10_1016_j_patcog_2016_09_011 crossref_primary_10_1109_ACCESS_2020_2984941 crossref_primary_10_1109_TGRS_2016_2643166 crossref_primary_10_1016_j_cviu_2013_03_007 crossref_primary_10_1049_iet_ipr_2016_0391 crossref_primary_10_1007_s00521_016_2269_9 crossref_primary_10_1109_TIE_2016_2552147 crossref_primary_10_1016_j_neunet_2013_11_009 crossref_primary_10_1016_j_patcog_2019_01_015 crossref_primary_10_1109_TCSVT_2018_2879833 crossref_primary_10_1016_j_neucom_2020_04_138 crossref_primary_10_1109_TCYB_2017_2726079 crossref_primary_10_2139_ssrn_5183492 crossref_primary_10_1109_TAFFC_2018_2854166 crossref_primary_10_1109_TIP_2020_3024728 crossref_primary_10_1109_TCSVT_2020_2989659 crossref_primary_10_1109_TIP_2015_2472277 crossref_primary_10_1016_j_ins_2016_06_029 crossref_primary_10_3390_rs11091039 crossref_primary_10_1109_TKDE_2018_2880448 crossref_primary_10_1007_s10462_018_9673_8 crossref_primary_10_1007_s13042_019_00992_9 crossref_primary_10_1007_s11263_017_1007_9 crossref_primary_10_1016_j_neucom_2015_06_064 crossref_primary_10_3390_app8112175 crossref_primary_10_1016_j_optlaseng_2023_107526 crossref_primary_10_1109_TASLP_2015_2412466 crossref_primary_10_1007_s00138_013_0536_7 crossref_primary_10_1049_iet_ipr_2017_0251 crossref_primary_10_1007_s11042_016_3500_5 crossref_primary_10_1016_j_jvcir_2020_102763 crossref_primary_10_1016_j_neucom_2015_06_096 crossref_primary_10_1109_TCSVT_2016_2527380 crossref_primary_10_1109_TGRS_2019_2952383 crossref_primary_10_1186_s12859_021_04333_y crossref_primary_10_1109_TAFFC_2021_3077489 crossref_primary_10_1016_j_ins_2018_05_048 crossref_primary_10_1016_j_ins_2020_08_097 crossref_primary_10_3390_rs13071372 crossref_primary_10_1007_s11265_014_0907_y crossref_primary_10_1016_j_neucom_2019_10_074 crossref_primary_10_1007_s11042_014_2257_y crossref_primary_10_1016_j_sigpro_2020_107569 crossref_primary_10_1109_TCSVT_2018_2856827 crossref_primary_10_1109_ACCESS_2017_2739807 crossref_primary_10_1109_TPAMI_2015_2462360 crossref_primary_10_1109_TCSVT_2017_2694060 crossref_primary_10_1109_TCYB_2018_2869789 crossref_primary_10_1109_TNNLS_2016_2545112 crossref_primary_10_1109_TGRS_2023_3294884 crossref_primary_10_1109_TIP_2016_2639438 crossref_primary_10_3390_electronics12194083 crossref_primary_10_1016_j_neucom_2017_07_003 crossref_primary_10_1109_TSIPN_2022_3169633 crossref_primary_10_1109_TIP_2015_2467315 crossref_primary_10_1016_j_engappai_2013_11_002 crossref_primary_10_1016_j_patcog_2018_03_027 crossref_primary_10_1109_TIP_2015_2485783 crossref_primary_10_1371_journal_pone_0173613 crossref_primary_10_1109_TPAMI_2019_2893953 crossref_primary_10_1016_j_eswa_2019_01_017 crossref_primary_10_1364_AO_56_006094 crossref_primary_10_1109_TIP_2018_2889960 crossref_primary_10_1016_j_neucom_2019_05_103 crossref_primary_10_1109_LSP_2014_2358853 crossref_primary_10_1016_j_knosys_2013_09_004 crossref_primary_10_1016_j_ins_2015_03_011 crossref_primary_10_1016_j_patcog_2014_09_002 crossref_primary_10_1109_TIP_2015_2447735 crossref_primary_10_1016_j_imavis_2014_07_001 crossref_primary_10_1016_j_neucom_2019_01_004 crossref_primary_10_1016_j_neucom_2020_04_063 crossref_primary_10_1007_s10489_022_03406_6 crossref_primary_10_1109_ACCESS_2018_2884027 crossref_primary_10_1016_j_knosys_2015_10_019 crossref_primary_10_1109_TMM_2017_2759500 crossref_primary_10_3390_math12111733 crossref_primary_10_1016_j_eswa_2016_12_012 crossref_primary_10_3390_app122211628 crossref_primary_10_1080_24725579_2020_1716115 crossref_primary_10_1007_s11263_014_0739_z crossref_primary_10_3390_rs9040335 crossref_primary_10_1109_TIP_2016_2554320 crossref_primary_10_1007_s13042_020_01203_6 crossref_primary_10_1016_j_neucom_2018_06_067 crossref_primary_10_1109_ACCESS_2018_2878553 crossref_primary_10_1007_s00521_023_09335_w crossref_primary_10_1109_TCYB_2018_2810806 crossref_primary_10_1016_j_ijleo_2015_08_168 crossref_primary_10_1016_j_patcog_2019_06_015 crossref_primary_10_3390_rs11131552 crossref_primary_10_1109_TIP_2015_2451953 crossref_primary_10_1016_j_neucom_2015_11_112 crossref_primary_10_1155_2022_1992429 crossref_primary_10_1109_TCSVT_2023_3266801 crossref_primary_10_1016_j_cosrev_2021_100374 crossref_primary_10_1109_JSTARS_2015_2508448 crossref_primary_10_1080_01431161_2016_1249302 crossref_primary_10_1109_LSP_2017_2764103 crossref_primary_10_1080_01431161_2020_1783711 crossref_primary_10_1109_TMM_2017_2685179 crossref_primary_10_1109_TMI_2020_2968770 crossref_primary_10_1016_j_patcog_2015_02_003 crossref_primary_10_1016_j_neucom_2016_05_021 crossref_primary_10_1016_j_knosys_2020_105482 crossref_primary_10_1007_s00521_015_2042_5 crossref_primary_10_1016_j_neucom_2014_11_067 crossref_primary_10_1007_s11432_015_5289_7 crossref_primary_10_1016_j_neucom_2016_05_039 crossref_primary_10_1016_j_neucom_2013_12_027 crossref_primary_10_1109_TCSVT_2015_2389413 crossref_primary_10_1007_s11042_020_09668_x crossref_primary_10_1016_j_sigpro_2014_08_020 crossref_primary_10_3390_math11102359 crossref_primary_10_1109_TMM_2013_2291214 crossref_primary_10_1016_j_neucom_2018_01_011 crossref_primary_10_1109_ACCESS_2017_2695239 crossref_primary_10_1109_TCSVT_2016_2613125 crossref_primary_10_1016_j_physa_2018_09_093 crossref_primary_10_1145_3078846 crossref_primary_10_1109_TMM_2020_3003747 crossref_primary_10_1109_TCSVT_2023_3241172 crossref_primary_10_1109_TNNLS_2019_2953675 crossref_primary_10_1109_JBHI_2022_3151333 crossref_primary_10_1016_j_patcog_2015_03_012 crossref_primary_10_3390_math9243259 crossref_primary_10_1016_j_dsp_2015_12_013 crossref_primary_10_1109_TNNLS_2017_2748952 crossref_primary_10_1371_journal_pcbi_1012448 crossref_primary_10_3390_s19122809 crossref_primary_10_1016_j_neucom_2015_07_139 crossref_primary_10_1007_s11760_014_0701_0 crossref_primary_10_1109_TCYB_2015_2433926 crossref_primary_10_1109_ACCESS_2018_2845298 crossref_primary_10_1007_s11042_015_2626_1 crossref_primary_10_1016_j_dsp_2013_11_013 crossref_primary_10_1109_TMM_2021_3111500 crossref_primary_10_1016_j_acha_2019_03_003 crossref_primary_10_1016_j_imavis_2016_10_009 crossref_primary_10_1016_j_patcog_2018_11_005 crossref_primary_10_1016_j_patcog_2022_109067 crossref_primary_10_1016_j_neucom_2014_12_083 crossref_primary_10_1016_j_neucom_2015_09_116 crossref_primary_10_1109_JSTARS_2020_3011431 crossref_primary_10_1109_TNNLS_2015_2490080 crossref_primary_10_1016_j_neucom_2015_01_035 crossref_primary_10_1016_j_inffus_2024_102546 crossref_primary_10_1007_s11042_017_4399_1 crossref_primary_10_1016_j_patcog_2015_11_016 crossref_primary_10_1007_s11063_019_10015_x crossref_primary_10_1109_TCYB_2018_2870487 crossref_primary_10_1109_TIP_2014_2344296 crossref_primary_10_1109_TCYB_2018_2802934 crossref_primary_10_1109_TNNLS_2017_2728060 crossref_primary_10_1109_TIP_2016_2623487 crossref_primary_10_1109_TCSVT_2020_2967424 crossref_primary_10_1016_j_neucom_2015_02_014 crossref_primary_10_1109_TPAMI_2019_2921031 crossref_primary_10_1007_s13042_019_01035_z crossref_primary_10_1145_2601408 crossref_primary_10_1016_j_gene_2019_04_060 crossref_primary_10_1016_j_neucom_2013_08_040 crossref_primary_10_1109_LGRS_2014_2362512 crossref_primary_10_1109_TNNLS_2020_2979685 crossref_primary_10_1007_s11042_019_08152_5 crossref_primary_10_1007_s11063_018_9809_5 crossref_primary_10_1109_TIP_2013_2259837 crossref_primary_10_1109_TKDE_2022_3144952 crossref_primary_10_1007_s13042_023_02012_3 crossref_primary_10_1007_s11431_021_1957_3 crossref_primary_10_1016_j_knosys_2015_06_001 crossref_primary_10_1155_2021_6699335 crossref_primary_10_1109_JSTSP_2016_2555239 crossref_primary_10_1109_TIP_2014_2316373 crossref_primary_10_1016_j_neucom_2014_11_028 crossref_primary_10_1109_LGRS_2015_2430871 crossref_primary_10_1155_2014_632871 crossref_primary_10_1109_TNNLS_2016_2572204 crossref_primary_10_1109_TMM_2014_2299516 crossref_primary_10_1016_j_neunet_2019_07_013 crossref_primary_10_1016_j_patcog_2018_05_024 crossref_primary_10_1016_j_apor_2023_103515 crossref_primary_10_1016_j_patcog_2016_12_015 crossref_primary_10_1016_j_ins_2019_03_012 crossref_primary_10_1016_j_patcog_2014_02_007 crossref_primary_10_1016_j_neucom_2015_12_130 crossref_primary_10_1016_j_jvcir_2014_12_002 crossref_primary_10_1109_TIP_2014_2311377 crossref_primary_10_1109_TIP_2019_2903294 crossref_primary_10_1109_ACCESS_2020_3021081 crossref_primary_10_1109_JSTARS_2014_2339298 crossref_primary_10_1109_JSTARS_2020_3014493 crossref_primary_10_1109_TIP_2014_2330792 crossref_primary_10_1016_j_compmedimag_2014_06_002 |
| Cites_doi | 10.1145/1390334.1390423 10.1145/1553374.1553431 10.1109/CVPR.2009.5206861 10.1145/1367497.1367542 10.1016/S0042-6989(97)00169-7 10.1007/s11222-007-9033-z 10.1109/CVPR.2010.5540033 10.1109/ICASSP.2009.4960432 10.1109/CVPR.2007.383172 10.1109/ICCV.2003.1238663 10.1109/CVPR.2006.68 10.1109/CVPR.2009.5206816 10.1109/CVPR.2008.4587598 10.1109/TPAMI.2008.79 10.1109/CVPR.2009.5206757 10.1109/ICCV.2007.4408872 10.1109/CVPR.2009.5206866 10.1002/cpa.20124 10.1109/ICCV.2009.5459267 10.1109/CVPR.2010.5540018 10.1109/ICCV.2009.5459178 10.1109/CVPR.2010.5539943 10.1023/b:visi.0000029664.99615.94 10.1109/TSP.2006.881199 10.7551/mitpress/7503.003.0105 10.1007/s11263-010-0338-6 10.1109/cvpr.2010.5540139 10.1109/ICCV.2005.239 10.1109/CVPR.2010.5539963 10.1007/978-3-540-88690-7_52 10.1145/1873951.1874164 10.1109/ICCV.2009.5459452 10.1007/978-3-642-15561-1_1 10.1145/1646396.1646452 10.1145/1143844.1143847 10.1109/CVPR.2009.5206851 10.1109/TIP.2006.881969 10.7551/mitpress/7503.003.0205 10.1111/j.1467-9868.2005.00532.x |
| ContentType | Journal Article |
| Copyright | 2015 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2013 |
| Copyright_xml | – notice: 2015 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2013 |
| 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.2012.63 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) 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 - Academic Technology Research Database MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Applied Sciences |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 104 |
| ExternalDocumentID | 2825187981 22392702 27677736 10_1109_TPAMI_2012_63 6165311 |
| Genre | orig-research 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 AAYOK IQODW RIG CGR CUY CVF ECM EIF NPM 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3 7X8 |
| ID | FETCH-LOGICAL-c470t-dbe4ef3493b2de7d326de7af418c334d85b50d66a9b44f4ee8dbba1a1cfb95b83 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 308 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000311127700010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Sun Sep 28 09:00:39 EDT 2025 Sat Sep 27 22:00:28 EDT 2025 Sun Nov 09 08:15:41 EST 2025 Thu Apr 03 06:56:41 EDT 2025 Wed Apr 02 07:35:46 EDT 2025 Tue Nov 18 22:30:36 EST 2025 Sat Nov 29 08:12:21 EST 2025 Tue Aug 26 16:41:23 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Hypergraph Computer vision Image processing Laplacian sparse coding locality preserving Locality Information retrieval Laplacian Bag of words Codebook semi-auto image tagging Signal quantization Tagging hypergraph Laplacian sparse coding Sparse representation Robustness Image classification |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c470t-dbe4ef3493b2de7d326de7af418c334d85b50d66a9b44f4ee8dbba1a1cfb95b83 |
| 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 | 22392702 |
| PQID | 1204652069 |
| PQPubID | 85458 |
| PageCount | 13 |
| ParticipantIDs | crossref_citationtrail_10_1109_TPAMI_2012_63 proquest_journals_1204652069 proquest_miscellaneous_1315134942 proquest_miscellaneous_1266711611 pascalfrancis_primary_27677736 ieee_primary_6165311 pubmed_primary_22392702 crossref_primary_10_1109_TPAMI_2012_63 |
| PublicationCentury | 2000 |
| PublicationDate | 2013-Jan. 2013-01-00 2013 2013-Jan 20130101 |
| PublicationDateYYYYMMDD | 2013-01-01 |
| PublicationDate_xml | – month: 01 year: 2013 text: 2013-Jan. |
| PublicationDecade | 2010 |
| 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 | 2013 |
| 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 | ref12 Chen (ref22) 2010 ref15 Mairal (ref13) ref14 ref53 ref52 ref11 ref10 Kavukcuoglu (ref48) 2008 ref16 Friedman (ref18) 2010 Donoho (ref2) 2004 ref46 ref45 ref47 Mosci (ref19) ref42 ref41 Kim (ref21) ref44 Lee (ref34) ref49 ref8 Griffin (ref43) 2007 ref7 ref9 Bengio (ref17) ref4 ref3 ref6 ref5 ref35 Chen (ref54) ref37 ref36 ref31 ref30 ref33 ref32 Ameesh (ref51) 2010; 90 ref38 Mairal (ref39) 2010; 11 Ameesh (ref50) ref24 ref23 ref26 ref25 ref20 Boureau (ref40) ref28 ref27 Donoho (ref1) 2004 Liu (ref29) |
| References_xml | – ident: ref33 doi: 10.1145/1390334.1390423 – ident: ref20 doi: 10.1145/1553374.1553431 – ident: ref46 doi: 10.1109/CVPR.2009.5206861 – volume-title: technical report year: 2010 ident: ref18 article-title: A Note on the Group Lasso and a Sparse Group Lasso – ident: ref32 doi: 10.1145/1367497.1367542 – ident: ref36 doi: 10.1016/S0042-6989(97)00169-7 – ident: ref35 doi: 10.1007/s11222-007-9033-z – volume-title: Proc. Advances in Neural Information Processing Systems ident: ref17 article-title: Group Sparse Coding – volume-title: technical report year: 2007 ident: ref43 article-title: Caltech-256 Object Category Data Set – volume-title: Proc. Advances in Neural Information Processing Systems ident: ref19 article-title: A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups – ident: ref52 doi: 10.1109/CVPR.2010.5540033 – ident: ref53 doi: 10.1109/ICASSP.2009.4960432 – volume-title: technical report year: 2010 ident: ref22 article-title: An Efficient Proximal-Gradient Method for Single and Multi-Task Regression with Structured Sparsity – ident: ref26 doi: 10.1109/CVPR.2007.383172 – ident: ref23 doi: 10.1109/ICCV.2003.1238663 – ident: ref27 doi: 10.1109/CVPR.2006.68 – ident: ref30 doi: 10.1109/CVPR.2009.5206816 – volume-title: Proc. Advances in Neural Information Processing Systems ident: ref13 article-title: Supervised Dictionary Learning – ident: ref25 doi: 10.1109/CVPR.2008.4587598 – ident: ref10 doi: 10.1109/TPAMI.2008.79 – ident: ref8 doi: 10.1109/CVPR.2009.5206757 – volume-title: technical report year: 2004 ident: ref2 article-title: For Most Large Underdetermined Systems of Linear Equations, the Minimal l1-Norm Near-Solution Approximates the Sparsest Near-Solution – volume-title: Proc. Int’l Conf. Machine Learning ident: ref21 article-title: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity – ident: ref45 doi: 10.1109/ICCV.2007.4408872 – ident: ref11 doi: 10.1109/CVPR.2009.5206866 – ident: ref3 doi: 10.1002/cpa.20124 – ident: ref12 doi: 10.1109/ICCV.2009.5459267 – ident: ref14 doi: 10.1109/CVPR.2010.5540018 – ident: ref24 doi: 10.1109/ICCV.2009.5459178 – ident: ref15 doi: 10.1109/CVPR.2010.5539943 – ident: ref42 doi: 10.1023/b:visi.0000029664.99615.94 – volume-title: Proc. Conf. Uncertainty in Artificial Intelligence ident: ref54 article-title: Super-Samples from Kernel Herding – ident: ref6 doi: 10.1109/TSP.2006.881199 – volume-title: Proc. Advances in Neural Information Processing Systems ident: ref34 article-title: Efficient Sparse Coding Algorithms doi: 10.7551/mitpress/7503.003.0105 – volume: 90 start-page: 88 year: 2010 ident: ref51 article-title: A New Baseline for Image Annotation publication-title: Int’l J. Computer Vision doi: 10.1007/s11263-010-0338-6 – ident: ref4 doi: 10.1109/cvpr.2010.5540139 – ident: ref29 article-title: Textual Query of Consumer Photos Facilitated by Large-Scale Web Data publication-title: IEEE Trans. Pattern Analysis and Machine Intelligence – ident: ref28 doi: 10.1109/ICCV.2005.239 – volume-title: Proc. Int’l Conf. Machine Learning ident: ref40 article-title: A Theoretical Analysis of Feature Pooling in Visual Recognition – ident: ref41 doi: 10.1109/CVPR.2010.5539963 – ident: ref44 doi: 10.1007/978-3-540-88690-7_52 – volume: 11 start-page: 19 year: 2010 ident: ref39 article-title: Online Learning for Matrix Factorization and Sparse Coding publication-title: J. Machine Learning Research – ident: ref31 doi: 10.1145/1873951.1874164 – ident: ref5 doi: 10.1109/ICCV.2009.5459452 – volume-title: Proc. European Conf. Computer Vision ident: ref50 article-title: A New Baseline for Image Annotation – ident: ref9 doi: 10.1007/978-3-642-15561-1_1 – ident: ref49 doi: 10.1145/1646396.1646452 – ident: ref38 doi: 10.1145/1143844.1143847 – volume-title: technical report year: 2004 ident: ref1 article-title: For Most Large Underdetermined Systems of Linear Equations, the Minimal l1-Norm Solution Is Also the Sparsest Solution – ident: ref47 doi: 10.1109/CVPR.2009.5206851 – ident: ref7 doi: 10.1109/TIP.2006.881969 – volume-title: technical report year: 2008 ident: ref48 article-title: Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition – ident: ref37 doi: 10.7551/mitpress/7503.003.0205 – ident: ref16 doi: 10.1111/j.1467-9868.2005.00532.x |
| SSID | ssj0014503 |
| Score | 2.561723 |
| Snippet | Sparse coding exhibits good performance in many computer vision applications. However, due to the overcomplete codebook and the independent coding process, the... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 92 |
| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Coding Coding, codes Computer science; control theory; systems Data Compression - methods Data processing. List processing. Character string processing Decision Support Techniques Encoding Exact sciences and technology Graphs hypergraph Laplacian sparse coding Image classification Image coding Image Interpretation, Computer-Assisted - methods Image reconstruction Information retrieval. Graph Information, signal and communications theory Laplace equations Laplacian sparse coding locality preserving Memory organisation. Data processing Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Preserves Quantization Representations Robustness semi-auto image tagging Signal and communications theory Similarity Software Sparse matrices Studies Tagging Telecommunications and information theory Theoretical computing |
| Title | Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications |
| URI | https://ieeexplore.ieee.org/document/6165311 https://www.ncbi.nlm.nih.gov/pubmed/22392702 https://www.proquest.com/docview/1204652069 https://www.proquest.com/docview/1266711611 https://www.proquest.com/docview/1315134942 |
| Volume | 35 |
| WOSCitedRecordID | wos000311127700010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| 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://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB7S0EN7aNqkD7fp4kLpaZ3YlixZxyU0pNCEQFPYm5GsERSCN-yjv78zstdJoFvoyQbNQcyDmdE8PoDPDGdsQi0ydFpnUoU8M5SHZM47LFuDvOQpgk3oq6t6PjfXezAdZ2EQMTaf4Qn_xlq-X7Qbfio7VYUilaFc54nWqp_VGisGsoooyBTBkIVTGnG_T_P05np2-Y27uMoTxbg55BENj2E9ckURW4U7I-2KmBN6VIvdYWd0P-cH_3fxl_BiCDPTWa8Xr2APu0M42EI4pINFH8LzB_sIj-Dyu-UeLdKY9McdZbyYni3Yt03TC0pXl3G5dbqTxnY-nT2oh7-Gn-dfb84usgFvIWulztcZiUdiENIIV3rUniI7-tggi7oVQvq6clXulbLGSRkkYu2ds4Ut2uBM5WrxBva7RYfvIK28k4Hcr2otBTheOltbK5QP2mMogk5gumV90w7LyBkT47aJSUlumii0hoXWKJHAl5H8rt_CsYvwiLk_Eg2MT2DySK7jeamV1lqoBI63gm4GI141RZlLVZW5Mgl8Go_J_LimYjtcbJhGKV1Q2Fz8g0ZQWMVrgMoE3vZKdH-BQRff__3iH-BZGfE3-M3nGPbXyw1-hKft7_Wv1XJCdjCvJ9EO_gAa7gOl |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dSxwxEB_ECtaH2mrVba1dofTpVneTbLJ5PEQ58e4QPMG3JdkkUCh7ch_-_U6ye6tCr-DTLmQewnwwM5mPH8AvD2csXUETq4VIGHdpIjEPSbTRllTS-iVPAWxCjMfFw4O83YBeNwtjrQ3NZ_bM_4ZavplWS_9Uds4zjiqDuc6HnDGSNtNaXc2A5QEHGWMYtHFMJF42ap5Pbvuja9_HRc64R85Bnyj9INYbZxTQVXxvpJoje1yDa7E-8AwO6Gr3fVf_DJ_aQDPuN5rxBTZsvQe7KxCHuLXpPdh5tZFwH0ZD5bu0UGfiu0fMeW18MfXerRcPMGGdhfXW8VoaVZu4_6oi_hXury4nF4OkRVxIKibSRYICYtZRJqkmxgqDsR1-lGNZUVHKTJHrPDWcK6kZc8zawmitMpVVTstcF_QANutpbY8gzo1mDh0wrxSGOIZpVShFuXHCWJc5EUFvxfqyateRe1SMv2VIS1JZBqGVXmglpxH87sgfmz0c6wj3Pfc7opbxEZy8kWt3TgQXQlAewfFK0GVrxvMyIynjOUm5jOC0O0YD9FUVVdvp0tNwLjIMnLP_0FAMrPwiIBLBYaNELxdodfHbvy_-E7YHk9GwHF6Pb77DRxLQOPwL0DFsLmZL-wO2qqfFn_nsJFjDM5syBgQ |
| 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=Laplacian+Sparse+Coding%2C+Hypergraph+Laplacian+Sparse+Coding%2C+and+Applications&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=SHENGHUA+GAO&rft.au=TSANG%2C+Ivor+Wai-Hung&rft.au=CHIA%2C+Liang-Tien&rft.date=2013&rft.pub=IEEE+Computer+Society&rft.issn=0162-8828&rft.volume=35&rft.issue=1&rft.spage=92&rft.epage=104&rft_id=info:doi/10.1109%2FTPAMI.2012.63&rft.externalDBID=n%2Fa&rft.externalDocID=27677736 |
| 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 |