Efficient Mean-shift Clustering Using Gaussian KD-Tree
Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed...
Uloženo v:
| Vydáno v: | Computer graphics forum Ročník 29; číslo 7; s. 2065 - 2073 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford, UK
Blackwell Publishing Ltd
01.09.2010
|
| Témata: | |
| ISSN: | 0167-7055, 1467-8659 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD‐tree in a high‐dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time‐varying sequences segmentation. |
|---|---|
| AbstractList | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD‐tree in a high‐dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time‐varying sequences segmentation. AbstractMean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences segmentation. |
| Author | Xiao, Chunxia Liu, Meng |
| Author_xml | – sequence: 1 givenname: Chunxia surname: Xiao fullname: Xiao, Chunxia organization: The School of Computer, Wuhan University, Wuhan, 430072, China – sequence: 2 givenname: Meng surname: Liu fullname: Liu, Meng organization: The School of Computer, Wuhan University, Wuhan, 430072, China |
| BookMark | eNqNkUFP7CAUhYnRxNHnf2hcuekIpRRYaGLqOL7o04UaEzc3lAFlrFShjeO_l74xLlzJAm7gfOfCYQdt-s4bhDKCpySNw-WUlBXPRcXktMBpFxMu6XS1gSbfB5togkmqOWZsG-3EuMQYl7xiE1TNrHXaGd9n_4zyeXxyts_qdoi9Cc4_ZndxnOdqiNEpn12c5rfBmD9oy6o2mr2vdRfdnc1u6_P88nr-tz65zHVJCM21pBTThsiFbKqSF1yokotGctrQAi_YwkrTWK0NazRf4FLZhgmiuLTGFloQuosO1r6voXsbTOzhxUVt2lZ50w0R0qtIIUtZjdL9H9JlNwSfbgecCVGVghdJJNYiHboYg7HwGtyLCh9AMIx5whLG2GCMDcY84X-esEro8Q9Uu171rvN9UK79jcHR2uDdtebj142hnp-NVeLzNe_S36y-eRWeoeKUM7i_msNDzc4pv6FwTz8BiFeeiQ |
| CitedBy_id | crossref_primary_10_1007_s10032_017_0285_7 crossref_primary_10_1109_TIP_2016_2547185 crossref_primary_10_1109_TVCG_2017_2759265 crossref_primary_10_1007_s11554_017_0733_0 crossref_primary_10_1109_ACCESS_2021_3114223 crossref_primary_10_1109_TCYB_2016_2626295 crossref_primary_10_1186_1687_6180_2011_111 crossref_primary_10_3390_rs5073259 crossref_primary_10_1016_j_patcog_2020_107452 crossref_primary_10_1007_s00371_014_0919_4 crossref_primary_10_1109_TVCG_2013_60 crossref_primary_10_1111_cgf_12502 crossref_primary_10_1111_cgf_12998 crossref_primary_10_3389_fnins_2018_00135 crossref_primary_10_1002_spe_70021 crossref_primary_10_1007_s41095_015_0024_2 crossref_primary_10_1109_TCAD_2023_3274922 crossref_primary_10_1109_TVCG_2010_226 crossref_primary_10_1007_s00138_013_0503_3 crossref_primary_10_1007_s11517_014_1240_0 |
| Cites_doi | 10.1007/978-3-540-24671-8_19 10.1145/1531326.1531327 10.1109/TPAMI.2003.1195991 10.1016/j.imavis.2003.08.005 10.1109/CGO.2007.13 10.1109/TPAMI.2003.1240123 10.1145/566570.566650 10.1145/1661412.1618454 10.1145/293347.293348 10.1145/1399504.1360635 10.1007/11744085_44 10.1145/1360612.1360639 10.1109/CVPR.2007.383228 10.1145/127719.122740 10.1109/ICCV.2003.1238383 10.1109/34.400568 10.1145/1073204.1073233 10.1145/1186562.1015763 10.1109/34.1000236 10.1109/ICCV.2003.1238382 10.1109/TIT.1975.1055330 10.1145/882262.882368 10.1145/882262.882367 10.1007/s00371-009-0394-5 |
| ContentType | Journal Article |
| Copyright | 2010 The Author(s) Journal compilation © 2010 The Eurographics Association and Blackwell Publishing Ltd. |
| Copyright_xml | – notice: 2010 The Author(s) Journal compilation © 2010 The Eurographics Association and Blackwell Publishing Ltd. |
| DBID | BSCLL AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D F28 FR3 |
| DOI | 10.1111/j.1467-8659.2010.01793.x |
| DatabaseName | Istex CrossRef Computer and Information Systems 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 |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional Engineering Research Database ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | CrossRef Technology Research Database Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1467-8659 |
| EndPage | 2073 |
| ExternalDocumentID | 2165328711 10_1111_j_1467_8659_2010_01793_x CGF1793 ark_67375_WNG_ZC5H37S3_W |
| Genre | article Feature |
| GroupedDBID | .3N .4S .DC .GA .Y3 05W 0R~ 10A 15B 1OB 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABPVW ACAHQ ACBWZ ACCZN ACFBH ACGFS ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AEMOZ AENEX AEUYR AEYWJ AFBPY AFEBI AFFNX AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHEFC AHQJS AIDQK AIDYY AIQQE AITYG AIURR AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG COF CS3 CWDTD D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBO EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 F5P FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RDJ RIWAO RJQFR ROL RX1 SAMSI SUPJJ TH9 TN5 TUS UB1 V8K W8V W99 WBKPD WIH WIK WOHZO WQJ WXSBR WYISQ WZISG XG1 ZL0 ZZTAW ~IA ~IF ~WT ALUQN AAYXX CITATION O8X 7SC 8FD JQ2 L7M L~C L~D F28 FR3 |
| ID | FETCH-LOGICAL-c4113-c93303b19d9b647278a478b973b320d5df9ebfcce5bc7d04afb581a79fef2c813 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 37 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000283159400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0167-7055 |
| IngestDate | Sun Nov 09 11:17:08 EST 2025 Fri Jul 25 23:29:09 EDT 2025 Sat Nov 29 03:41:06 EST 2025 Tue Nov 18 22:00:54 EST 2025 Wed Aug 20 07:24:30 EDT 2025 Tue Nov 11 03:33:40 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4113-c93303b19d9b647278a478b973b320d5df9ebfcce5bc7d04afb581a79fef2c813 |
| Notes | ark:/67375/WNG-ZC5H37S3-W istex:F386FF631523E7F3CE723560CBFD898EC5B9D87E ArticleID:CGF1793 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
| PQID | 758864872 |
| PQPubID | 30877 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_1671294961 proquest_journals_758864872 crossref_primary_10_1111_j_1467_8659_2010_01793_x crossref_citationtrail_10_1111_j_1467_8659_2010_01793_x wiley_primary_10_1111_j_1467_8659_2010_01793_x_CGF1793 istex_primary_ark_67375_WNG_ZC5H37S3_W |
| PublicationCentury | 2000 |
| PublicationDate | September 2010 |
| PublicationDateYYYYMMDD | 2010-09-01 |
| PublicationDate_xml | – month: 09 year: 2010 text: September 2010 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford, UK |
| PublicationPlace_xml | – name: Oxford, UK – name: Oxford |
| PublicationTitle | Computer graphics forum |
| PublicationYear | 2010 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | Carreira-Perpinán M.: Acceleration strategies for Gaussian mean-shift image segmentation. In CVPR (2006), vol. 1. Fukunaga K., Hostetler L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21, 1 (1975), 32-40. Wuhrer S., Brunton A.: Segmenting animated objects into near-rigid components. The Visual Computer 26, 2 (2010), 147-155. Barash D., Comaniciu D.: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Vision Computing 22, 1 (2004), 73-81. Cheng Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 8 (1995), 790-799. Elgammal A., Duraiswami R., Davis L.: Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 11 (2003), 1499-1504. Comaniciu D., Ramesh V., Meer P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 5 (2003), 564-577. Arya S., Mount D., Netanyahu N., Silverman R., WU A.: An optimal algorithm for approximate near-est neighbor searching fixed dimensions. Journal of the ACM (JACM) 45, 6 (1998), 891-923. Adams A., Gelfand N., Dolson L., Levoy M.: Gaussian kd-trees for fast high-dimensional filtering. ACM Transactions on Graphics (TOG) 28, 3 (2009), 21. Comaniciu D., Meer P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence 24, 5 (2002), 603-619. DeMenthon D.: Spatio-temporal segmentation of video by hierarchical mean shift analysis. Language 2 (2002). Wang L., Bhat P., Colburn R., Agrawala M., Cohen M.: Video cutout. ACM Transactions on Graphics 24, 3 (2005), 585-594. Hanrahan P., Salzman D., Aupperle L.: A rapid hierarchical radiosity algorithm. ACM SIGGRAPH Computer Graphics 25, 4 (1991), 206. Buades A., Coll B., Morel J.: A non-local algorithm for image denoising. In CVPR 2005 (2005), pp. 60-65. Fleishman S., Drori I., Cohen-Or D.: Bilateral mesh denoising. ACM Transactions on Graphics (TOG) 22, 3 (2003), 950-953. Jones T., Durand F., Desbrun M.: Non-iterative, feature-preserving mesh smoothing. ACM Transactions on Graphics 22, 3 (2003), 943-949. An X., Pellacini F.: Appprop: all-pairs appearances-pace edit propagation. ACM Trans. Graph 27, 3 (2008), 40. 2004; 22 1995; 17 2010 2009 2002; 2 2008 2007 2006 2005 2004 2006; 1 2003 2002 2004; 1 1998; 45 2005; 2005 2005; 24 2009; 28 2010; 26 1991; 25 2005; 243 2002; 24 2008; 27 2003; 25 2003; 3 1975; 21 2003; 22 e_1_2_7_5_2 e_1_2_7_4_2 DeCarlo D. (e_1_2_7_13_2) 2002 e_1_2_7_3_2 e_1_2_7_2_2 e_1_2_7_9_2 Wei Y. (e_1_2_7_30_2) 2004 e_1_2_7_8_2 e_1_2_7_7_2 Wang P. (e_1_2_7_29_2) 2007 e_1_2_7_19_2 e_1_2_7_18_2 DeMenthon D. (e_1_2_7_12_2) 2002; 2 e_1_2_7_17_2 Carreira‐Perpinán M. (e_1_2_7_10_2) 2006; 1 e_1_2_7_16_2 LI X. (e_1_2_7_22_2) 2005 e_1_2_7_15_2 Horn D. (e_1_2_7_20_2) 2007 e_1_2_7_14_2 e_1_2_7_11_2 Buades A. (e_1_2_7_6_2) 2005; 2005 e_1_2_7_26_2 e_1_2_7_27_2 e_1_2_7_28_2 Yamauchi H. (e_1_2_7_37_2) 2005 e_1_2_7_25_2 e_1_2_7_24_2 e_1_2_7_23_2 e_1_2_7_31_2 e_1_2_7_32_2 e_1_2_7_21_2 e_1_2_7_33_2 e_1_2_7_34_2 e_1_2_7_36_2 e_1_2_7_38_2 Yang C. (e_1_2_7_35_2) 2003 |
| References_xml | – reference: Comaniciu D., Meer P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence 24, 5 (2002), 603-619. – reference: An X., Pellacini F.: Appprop: all-pairs appearances-pace edit propagation. ACM Trans. Graph 27, 3 (2008), 40. – reference: Fukunaga K., Hostetler L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21, 1 (1975), 32-40. – reference: Wuhrer S., Brunton A.: Segmenting animated objects into near-rigid components. The Visual Computer 26, 2 (2010), 147-155. – reference: Wang L., Bhat P., Colburn R., Agrawala M., Cohen M.: Video cutout. ACM Transactions on Graphics 24, 3 (2005), 585-594. – reference: Hanrahan P., Salzman D., Aupperle L.: A rapid hierarchical radiosity algorithm. ACM SIGGRAPH Computer Graphics 25, 4 (1991), 206. – reference: Carreira-Perpinán M.: Acceleration strategies for Gaussian mean-shift image segmentation. In CVPR (2006), vol. 1. – reference: Elgammal A., Duraiswami R., Davis L.: Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 11 (2003), 1499-1504. – reference: Barash D., Comaniciu D.: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Vision Computing 22, 1 (2004), 73-81. – reference: Cheng Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 8 (1995), 790-799. – reference: DeMenthon D.: Spatio-temporal segmentation of video by hierarchical mean shift analysis. Language 2 (2002). – reference: Buades A., Coll B., Morel J.: A non-local algorithm for image denoising. In CVPR 2005 (2005), pp. 60-65. – reference: Fleishman S., Drori I., Cohen-Or D.: Bilateral mesh denoising. ACM Transactions on Graphics (TOG) 22, 3 (2003), 950-953. – reference: Jones T., Durand F., Desbrun M.: Non-iterative, feature-preserving mesh smoothing. ACM Transactions on Graphics 22, 3 (2003), 943-949. – reference: Adams A., Gelfand N., Dolson L., Levoy M.: Gaussian kd-trees for fast high-dimensional filtering. ACM Transactions on Graphics (TOG) 28, 3 (2009), 21. – reference: Arya S., Mount D., Netanyahu N., Silverman R., WU A.: An optimal algorithm for approximate near-est neighbor searching fixed dimensions. Journal of the ACM (JACM) 45, 6 (1998), 891-923. – reference: Comaniciu D., Ramesh V., Meer P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 5 (2003), 564-577. – volume: 1 year: 2006 article-title: Acceleration strategies for Gaussian mean‐shift image segmentation publication-title: CVPR – volume: 22 start-page: 943 issue: 3 year: 2003 end-page: 949 article-title: Non‐iterative, feature‐preserving mesh smoothing publication-title: ACM Transactions on Graphics – start-page: 769 year: 2002 end-page: 776 – volume: 243 year: 2005 – start-page: 1 year: 2007 end-page: 8 – volume: 3 start-page: 447 year: 2003 end-page: 450 – volume: 17 start-page: 790 issue: 8 year: 1995 end-page: 799 article-title: Mean shift, mode seeking, and clustering publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 574 year: 2004 end-page: 583 – year: 2007 – start-page: 664 year: 2003 end-page: 671 – volume: 27 start-page: 40 issue: 3 year: 2008 article-title: Appprop: all‐pairs appearances‐pace edit propagation publication-title: ACM Trans. Graph – volume: 2005 start-page: 60 year: 2005 end-page: 65 article-title: A non‐local algorithm for image denoising publication-title: CVPR – volume: 24 start-page: 603 issue: 5 year: 2002 end-page: 619 article-title: Mean shift: A robust approach toward feature space analysis publication-title: IEEE Transactions on pattern analysis and machine intelligence – volume: 28 start-page: 21 issue: 3 year: 2009 article-title: Gaussian kd‐trees for fast high‐dimensional filtering publication-title: ACM Transactions on Graphics (TOG) – volume: 21 start-page: 32 issue: 1 year: 1975 end-page: 40 article-title: The estimation of the gradient of a density function, with applications in pattern recognition publication-title: IEEE Transactions on Information Theory – volume: 2 year: 2002 article-title: Spatio‐temporal segmentation of video by hierarchical mean shift analysis publication-title: Language – start-page: 217 year: 2005 – year: 2010 – volume: 1 start-page: 106 year: 2004 end-page: 113 – volume: 22 start-page: 950 issue: 3 year: 2003 end-page: 953 article-title: Bilateral mesh denoising publication-title: ACM Transactions on Graphics (TOG) – volume: 22 start-page: 73 issue: 1 year: 2004 end-page: 81 article-title: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift publication-title: Image and Vision Computing – start-page: 1 year: 2009 end-page: 6 – start-page: 1 year: 2008 end-page: 11 – volume: 45 start-page: 891 issue: 6 year: 1998 end-page: 923 article-title: An optimal algorithm for approximate near‐est neighbor searching fixed dimensions publication-title: Journal of the ACM (JACM) – volume: 25 start-page: 564 issue: 5 year: 2003 end-page: 577 article-title: Kernel‐based object tracking publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – year: 2006 – start-page: 238 year: 2004 end-page: 249 – volume: 25 start-page: 206 issue: 4 year: 1991 article-title: A rapid hierarchical radiosity algorithm publication-title: ACM SIGGRAPH Computer Graphics – start-page: 456 year: 2003 end-page: 463 – volume: 25 start-page: 1499 issue: 11 year: 2003 end-page: 1504 article-title: Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 174 year: 2007 – volume: 24 start-page: 585 issue: 3 year: 2005 end-page: 594 article-title: Video cutout publication-title: ACM Transactions on Graphics – volume: 26 start-page: 147 issue: 2 year: 2010 end-page: 155 article-title: Segmenting animated objects into near‐rigid components publication-title: The Visual Computer – start-page: 17 year: 2007 – volume: 2005 start-page: 60 year: 2005 ident: e_1_2_7_6_2 article-title: A non‐local algorithm for image denoising publication-title: CVPR – volume: 1 year: 2006 ident: e_1_2_7_10_2 article-title: Acceleration strategies for Gaussian mean‐shift image segmentation publication-title: CVPR – ident: e_1_2_7_31_2 doi: 10.1007/978-3-540-24671-8_19 – ident: e_1_2_7_2_2 doi: 10.1145/1531326.1531327 – ident: e_1_2_7_11_2 doi: 10.1109/TPAMI.2003.1195991 – ident: e_1_2_7_5_2 doi: 10.1016/j.imavis.2003.08.005 – ident: e_1_2_7_7_2 doi: 10.1109/CGO.2007.13 – ident: e_1_2_7_14_2 doi: 10.1109/TPAMI.2003.1240123 – start-page: 769 volume-title: SIGGRAPH year: 2002 ident: e_1_2_7_13_2 doi: 10.1145/566570.566650 – ident: e_1_2_7_33_2 doi: 10.1145/1661412.1618454 – ident: e_1_2_7_3_2 doi: 10.1145/293347.293348 – ident: e_1_2_7_38_2 doi: 10.1145/1399504.1360635 – ident: e_1_2_7_24_2 doi: 10.1007/11744085_44 – ident: e_1_2_7_4_2 doi: 10.1145/1360612.1360639 – ident: e_1_2_7_25_2 doi: 10.1109/CVPR.2007.383228 – ident: e_1_2_7_26_2 – ident: e_1_2_7_23_2 – ident: e_1_2_7_19_2 doi: 10.1145/127719.122740 – start-page: 106 volume-title: CVPR year: 2004 ident: e_1_2_7_30_2 – ident: e_1_2_7_34_2 – ident: e_1_2_7_36_2 doi: 10.1109/ICCV.2003.1238383 – ident: e_1_2_7_8_2 doi: 10.1109/34.400568 – start-page: 447 volume-title: ICIP year: 2003 ident: e_1_2_7_35_2 – ident: e_1_2_7_28_2 doi: 10.1145/1073204.1073233 – ident: e_1_2_7_32_2 doi: 10.1145/1186562.1015763 – ident: e_1_2_7_9_2 doi: 10.1109/34.1000236 – volume: 2 year: 2002 ident: e_1_2_7_12_2 article-title: Spatio‐temporal segmentation of video by hierarchical mean shift analysis publication-title: Language – ident: e_1_2_7_18_2 doi: 10.1109/ICCV.2003.1238382 – volume-title: Workshop on Artificial Intelligence and Statistics (AISTATS year: 2007 ident: e_1_2_7_29_2 – start-page: 217 volume-title: SGP year: 2005 ident: e_1_2_7_22_2 – volume-title: SMA year: 2005 ident: e_1_2_7_37_2 – ident: e_1_2_7_16_2 doi: 10.1109/TIT.1975.1055330 – ident: e_1_2_7_15_2 doi: 10.1145/882262.882368 – ident: e_1_2_7_17_2 – ident: e_1_2_7_21_2 doi: 10.1145/882262.882367 – start-page: 174 volume-title: Proceedings of the 2007 symposium on Interactive 3D graphics and games year: 2007 ident: e_1_2_7_20_2 – ident: e_1_2_7_27_2 doi: 10.1007/s00371-009-0394-5 |
| SSID | ssj0004765 |
| Score | 2.1413817 |
| Snippet | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications... AbstractMean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2065 |
| SubjectTerms | Approximation Clustering Computational efficiency Computer graphics Datasets Gaussian I.4 [Computing methodologies]: Image Processing and Computer Vision-Applications Image coding Image processing systems Interactive Mathematical models Normal distribution Segmentation Studies |
| Title | Efficient Mean-shift Clustering Using Gaussian KD-Tree |
| URI | https://api.istex.fr/ark:/67375/WNG-ZC5H37S3-W/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1467-8659.2010.01793.x https://www.proquest.com/docview/758864872 https://www.proquest.com/docview/1671294961 |
| Volume | 29 |
| WOSCitedRecordID | wos000283159400007&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: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1467-8659 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004765 issn: 0167-7055 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NTxsxEB1VpIf2AP2gYqGtthLitlUcr7-4VYEEqTRCBQTqxbK9tkCgDcomKEd-Ar-RX1LPZpMStQeEeltpPavdsWf9xn7zDLBNO9QwaosspzJkOWdFJo0KmWSecCeCY6QuFD4Ug4E8P1dHDf8Ja2Fm-hCLBTeMjPp_jQFubPV3kEvOVMPQwrH2NeLJFtZYxUSstfezd3r4p0pScDZX-kYNmWVezz-ftTRZtdDv0yUk-hjP1hNSb-1_fsobWG1gafptNo7ewgtfvoPXj8QK38Pufq02ESep9Ic35cPdfXVxGcZp93qCYguxTVrzD9K-mVRYmpl-34uNTkber8Npb_-ke5A1Jy9kLieEZg6XOaglqlAW9eWFNLmQVglqaaddsCIob4NznlkninZugmWSGKGCDx0nCf0AK-Ww9BuQtrlt84ghnHMRHFoZ0yvFnPIx1STK2pCAmLtYu0aWHE_HuNZL6YnQ6B2N3tG1d_Q0AbKwvJlJczzBZqfuxYWBGV0htU0wfTbo619ddkDFMdVnCWzNu1k3UV3pmFtJHjO8TgJfFndjOOIeiyn9cFLpOLgigsoVJwnwus-f_G662-_h1eZzDbfg1YzbgAy4j7AyHk38J3jpbseX1ehzEw6_AYg2BrI |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fb9MwED9N7STgARgwEcYgkxBvQXUd_-MNdWuL1lXT6LSJF8t2bDExZahp0R75CHxGPgm-NO1WwcOEeIsUX5Sc7-Lf2Xe_A3hDu9QwaosspzJkOWdFJo0KmWSecCeCY6QuFB6J8Vien6vjph0Q1sIs-CFWG27oGfX_Gh0cN6T_9HLJmWpStNDY3kVA2c45FbIF7f2T_unopkxScLak-kYSmfXEnr8-a221aqPir9eg6G1AW69I_Uf_9Vsew8MGmKYfFpa0BRu-fAIPbtEVPoX3BzXfRFym0iNvyl8_flZfLsIs7V3OkW4hjknrDIR0YOYVFmemh_tx0GTq_TM47R9MesOs6b2QuZwQmjnc6KCWqEJZZJgX0uRCWiWopd1OwYqgvA3OeWadKDq5CZZJYoQKPnSdJHQbWuVV6Z9D2uG2wyOKcM5FeGhlDLAUc8rHYJMoa0MCYqlj7RpicuyPcanXAhShUTsataNr7ejrBMhK8tuCnOMOMm_raVwJmOlXTG4TTJ-NB_pzjw2p-ET1WQI7y3nWjV9XOkZXkscYr5vA3upudEg8ZTGlv5pXOlpXxFC54iQBXk_6nd9N9wZ9vHrxr4Kv4d5wcjTSo4_jwx24v8h0wHy4l9CaTed-Fzbd99lFNX3V-MZvV8sKog |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB6hBCF64I3qloeREDejbNb74oaSOkUNUQWtWnFZ7a53RdXKreIE9dif0N_IL2HHcUIjOFSImyXvWPbsjueb3ZlvAN7SPjWM2jLLqQxZzlmZSaNCJpkn3IngGGkKhcdiMpHHx2q_bQeEtTALfojVhhtaRvO_RgP3F2X408olZ6pN0cLF9j4Cym7OFMs70B1-KQ7Hv8skBWdLqm8kkVlP7Pnrs9a8VRcVf7kGRW8C2sYjFQ__67c8ggctME0_LlbSY7jjqyewcYOu8Cl82Gn4JqKbSj97U_28uq6_n4RZOjibI91CHJM2GQjpyMxrLM5M94Zx0MHU-2dwWOwcDHaztvdC5nJCaOZwo4NaokplkWFeSJMLaZWglvZ7JSuD8jY455l1ouzlJlgmiREq-NB3ktDn0KnOK78JaY_bHo8owjkX4aGVMcBSzCkfg02irA0JiKWOtWuJybE_xpleC1CERu1o1I5utKMvEyAryYsFOcctZN4107gSMNNTTG4TTB9NRvrbgO1S8ZXqowS2l_OsW7uudYyuJI8xXj-BN6u70SDxlMVU_nxe67i6IobKFScJ8GbSb_1uejAq8GrrXwVfw739YaHHnyZ723B_keiA6XAvoDObzv1LuOt-zE7q6avWNH4B0BcKHQ |
| 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=Efficient+Mean%E2%80%90shift+Clustering+Using+Gaussian+KD%E2%80%90Tree&rft.jtitle=Computer+graphics+forum&rft.au=Xiao%2C+Chunxia&rft.au=Liu%2C+Meng&rft.date=2010-09-01&rft.issn=0167-7055&rft.eissn=1467-8659&rft.volume=29&rft.issue=7&rft.spage=2065&rft.epage=2073&rft_id=info:doi/10.1111%2Fj.1467-8659.2010.01793.x&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_j_1467_8659_2010_01793_x |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-7055&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-7055&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-7055&client=summon |