Eye movement prediction and variability on natural video data sets
We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Intersu...
Saved in:
| Published in | Visual cognition Vol. 20; no. 4-5; pp. 495 - 514 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Taylor & Francis Group
01.04.2012
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1350-6285 1464-0716 1464-0716 |
| DOI | 10.1080/13506285.2012.667456 |
Cover
| Abstract | We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Intersubject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms. |
|---|---|
| AbstractList | We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Inter-subject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms. We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Intersubject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms. We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Inter-subject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms.We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Inter-subject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms. |
| Author | Vig, Eleonora Dorr, Michael Barth, Erhardt |
| AuthorAffiliation | 2 Schepens Eye Research Institute, Department of Ophthalmology, Harvard Medical School, 20 Staniford St, Boston, MA 02114, USA, michael.dorr@schepens.harvard.edu 1 Institute for Neuro- and Bioinformatics, University of Lübeck, Ratzeburger Allee 160, D-23538 Lübeck, Germany, vig@inb.uni-luebeck.de , barth@inb.uni-luebeck.de |
| AuthorAffiliation_xml | – name: 1 Institute for Neuro- and Bioinformatics, University of Lübeck, Ratzeburger Allee 160, D-23538 Lübeck, Germany, vig@inb.uni-luebeck.de , barth@inb.uni-luebeck.de – name: 2 Schepens Eye Research Institute, Department of Ophthalmology, Harvard Medical School, 20 Staniford St, Boston, MA 02114, USA, michael.dorr@schepens.harvard.edu |
| Author_xml | – sequence: 1 givenname: Michael surname: Dorr fullname: Dorr, Michael email: michael.dorr@schepens.harvard.edu organization: Schepens Eye Research Institute, Department of Ophthalmology , Harvard Medical School – sequence: 2 givenname: Eleonora surname: Vig fullname: Vig, Eleonora organization: Institute for Neuro- and Bioinformatics , University of Lübeck – sequence: 3 givenname: Erhardt surname: Barth fullname: Barth, Erhardt organization: Institute for Neuro- and Bioinformatics , University of Lübeck |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22844203$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkUtv1DAUhS1URB_wDxDKkk2mfsfDAlSqQpEqsYG1dePYYJTYg-1MlX-PR2krYEFZ2bLPd3TPuafoKMRgEXpJ8IZghc8JE1hSJTYUE7qRsuNCPkEnhEve4o7Io3qvkvagOUanOf_AmEjViWfomFLFOcXsBL2_Wmwzxb2dbCjNLtnBm-JjaCAMzR6Sh96PvixNfQpQ5gRjs_eDjc0ABZpsS36OnjoYs31xd56hrx-uvlxetzefP366vLhpDd92paVbJRxshXOsJ6CcoJYy45wVlktQRuKBAzaOcbWVygwW9xz3lFnTiR4YsDMkVt857GC5hXHUu-QnSIsmWB860fed6EMneu2kcm9Xbjf3kx1MDVpTPLARvP7zJ_jv-lvca8YxV1xVg9d3Bin-nG0uevLZ2HGEYOOcNVFUCtEJwR-XUtF1qqtAlb76fayHee53UwV8FZgUc07W_W_cN39hxhc47LSG8-Nj8LsV9sHFNMFtTOOgCyxjTC5BMD5r9k-HX8Nxx14 |
| CitedBy_id | crossref_primary_10_3390_mti3010019 crossref_primary_10_1111_cgf_13150 crossref_primary_10_1109_TCSVT_2008_2005798 crossref_primary_10_1371_journal_pone_0093254 crossref_primary_10_3390_drones2040036 crossref_primary_10_1038_s42003_020_0771_1 crossref_primary_10_1038_s41598_020_66196_2 |
| Cites_doi | 10.1167/8.2.2 10.1109/TPAMI.2009.112 10.1016/0042-6989(90)90120-A 10.3167/proj.2008.020102 10.1145/1671954.1671958 10.1016/j.imavis.2010.07.001 10.1007/s11263-009-0275-4 10.1068/p2935 10.1167/10.10.28 10.1109/34.730558 10.1038/81887 10.1016/j.visres.2005.03.019 10.1006/cgip.1993.1033 10.1109/CVPR.2008.4587756 10.1167/11.5.5 10.1109/TIP.2009.2030969 10.1038/381607a0 10.1364/JOSAA.4.002379 10.1162/neco.2009.11-06-391 10.1109/TIP.2004.834657 10.1167/8.14.18 10.1016/S0042-6989(01)00102-X 10.1117/12.674147 10.1016/j.visres.2004.09.017 10.1016/j.visres.2005.10.002 10.1007/s12559-010-9074-z 10.1016/j.visres.2010.08.011 10.1167/8.7.32 10.1080/13506280444000661 10.1109/TPAMI.2006.86 10.1167/11.2.20 10.1163/15685680360511645 10.1080/13506280902764539 10.1163/156856809789476065 10.1007/s12559-010-9061-4 10.1088/0954-898X/10/4/304 10.1167/9.3.5 10.1016/j.visres.2005.12.005 10.1167/9.5.7 10.16910/jemr.2.2.6 |
| ContentType | Journal Article |
| Copyright | Copyright Psychology Press Ltd 2012 |
| Copyright_xml | – notice: Copyright Psychology Press Ltd 2012 |
| DBID | AAYXX CITATION NPM 7TK 7X8 5PM ADTOC UNPAY |
| DOI | 10.1080/13506285.2012.667456 |
| DatabaseName | CrossRef PubMed Neurosciences Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef PubMed Neurosciences Abstracts MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic Neurosciences Abstracts |
| 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: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology Psychology |
| EISSN | 1464-0716 |
| EndPage | 514 |
| ExternalDocumentID | oai:pubmedcentral.nih.gov:3404848 PMC3404848 22844203 10_1080_13506285_2012_667456 667456 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NEI NIH HHS grantid: R01 EY018664 – fundername: NEI NIH HHS grantid: R01 EY019281 – fundername: National Eye Institute : NEI grantid: R01 EY018664 || EY |
| GroupedDBID | --- -~X .7I .QK 0BK 0R~ 123 29Q 4.4 53G 5VS AAGDL AAGZJ AAHIA AAMFJ AAMIU AAPUL AATTQ AAZMC ABCCY ABDBF ABFIM ABIVO ABJNI ABLIJ ABPEM ABRYG ABTAI ABXUL ABXYU ABZLS ACGEJ ACGFS ACHQT ACPRK ACTIO ACTOA ACUHS ADAHI ADCVX ADKVQ ADXPE AECIN AEFOU AEISY AEKEX AEMXT AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AHDZW AIJEM AIYEW AJWEG AKBVH ALMA_UNASSIGNED_HOLDINGS ALQZU AQTUD AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CQ1 CS3 DGFLZ DKSSO DU5 EAP EBD EBO EBS EJD EMK EMOBN EPL EPS ESX E~B E~C F5P FEDTE G-F GTTXZ H13 HF~ HVGLF HZ~ IPNFZ J.O KYCEM M4Z NA5 NW- O9- P2P RIG RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TH9 TNTFI TRJHH TUROJ UT5 UT9 VAE ~01 ~S~ 07M 1TA 4B3 AANPH AAYXX ABBZI ABVXC ABWZE ACIKQ ACPKE ACRBO ADEWX ADIUE ADLFI ADXAZ AETEA AEXSR AIXGP ALLRG C5A CAG CBZAQ CITATION CKOZC COF C~T DGXZK EFRLQ EGDCR FXNIP JLMOS L7Y LJTGL QZZOY RBICI TBH UA1 UQL ADYSH NPM 7TK 7X8 5PM ADTOC UNPAY |
| ID | FETCH-LOGICAL-c497t-2985fa95ff3b1a8f52e23cffe5e46a8c60d4a0cf348968cde0b40b23ec75ba3a3 |
| IEDL.DBID | UNPAY |
| ISSN | 1350-6285 1464-0716 |
| IngestDate | Sun Oct 26 04:14:18 EDT 2025 Thu Aug 21 18:45:58 EDT 2025 Thu Sep 04 18:05:15 EDT 2025 Wed Oct 01 14:10:38 EDT 2025 Mon Jul 21 05:53:02 EDT 2025 Thu Apr 24 23:07:50 EDT 2025 Wed Oct 01 03:38:58 EDT 2025 Mon Oct 20 23:47:24 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4-5 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c497t-2985fa95ff3b1a8f52e23cffe5e46a8c60d4a0cf348968cde0b40b23ec75ba3a3 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://www.ncbi.nlm.nih.gov/pmc/articles/3404848 |
| PMID | 22844203 |
| PQID | 1257787826 |
| PQPubID | 23462 |
| PageCount | 20 |
| ParticipantIDs | informaworld_taylorfrancis_310_1080_13506285_2012_667456 proquest_miscellaneous_1257787826 crossref_primary_10_1080_13506285_2012_667456 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3404848 unpaywall_primary_10_1080_13506285_2012_667456 crossref_citationtrail_10_1080_13506285_2012_667456 proquest_miscellaneous_1826557554 pubmed_primary_22844203 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2012-04-00 |
| PublicationDateYYYYMMDD | 2012-04-01 |
| PublicationDate_xml | – month: 04 year: 2012 text: 2012-04-00 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Visual cognition |
| PublicationTitleAlternate | Vis cogn |
| PublicationYear | 2012 |
| Publisher | Taylor & Francis Group |
| Publisher_xml | – name: Taylor & Francis Group |
| References | Kienzle W. (CIT0023) 2007 CIT0030 CIT0032 CIT0031 CIT0033 Foulsham T. (CIT0012) CIT0036 Mota C. (CIT0034) 2000; 9 Jähne B. (CIT0020) 1999; 2 CIT0038 CIT0037 CIT0039 CIT0041 Barth E. (CIT0002) 2006; 6057 CIT0043 CIT0001 CIT0045 CIT0044 Nuthmann A. (CIT0035) Smith T. J. (CIT0042) 2008; 2 Itti L. (CIT0018) 2006; 19 CIT0003 CIT0047 CIT0046 CIT0005 Judd T. (CIT0021) 2009 CIT0049 CIT0004 CIT0048 CIT0007 CIT0006 CIT0009 CIT0008 CIT0050 CIT0010 CIT0054 CIT0053 CIT0055 CIT0014 CIT0013 CIT0016 CIT0015 CIT0017 CIT0019 Poynton C. (CIT0040) 2003 Wolfe J. M. (CIT0051) 1998 CIT0022 Field D. J. (CIT0011) 1987; 4 CIT0024 CIT0027 Koch C. (CIT0025) 1985; 4 CIT0026 Zelinsky G. (CIT0052) CIT0029 CIT0028 19210172 - Neural Comput. 2009 Jan;21(1):239-71 10695763 - Network. 1999 Nov;10(4):341-50 19709976 - IEEE Trans Image Process. 2010 Jan;19(1):185-98 21622729 - J Vis. 2011 May 27;11(5):5 2392840 - Vision Res. 1990;30(7):1111-7 19814903 - Spat Vis. 2009;22(5):397-408 16640265 - IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):802-17 20801147 - Vision Res. 2010 Oct 28;50(22):2190-9 22711998 - Vis cogn. 2012 Jan 1;20(4-5):515-545 16289663 - Vision Res. 2006 May;46(11):1762-76 11100157 - Nat Neurosci. 2000 Dec;3(12):1340-5 19146319 - J Vis. 2008 Nov 20;8(14):18.1-26 15935435 - Vision Res. 2005 Aug;45(18):2397-416 19146264 - J Vis. 2008 Dec 16;8(7):32.1-20 12696858 - Spat Vis. 2003;16(2):125-54 3430225 - J Opt Soc Am A. 1987 Dec;4(12):2379-94 19757944 - J Vis. 2009 Mar 13;9(3):5.1-24 19926907 - IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):171-7 20884493 - J Vis. 2010 Aug 26;10(10):28 11718795 - Vision Res. 2001;41(25-26):3559-65 3836989 - Hum Neurobiol. 1985;4(4):219-27 19757885 - J Vis. 2009 May 13;9(5):7.1-15 21367757 - J Vis. 2011 Feb 25;11(2):null 15462141 - IEEE Trans Image Process. 2004 Oct;13(10):1304-18 10755142 - Perception. 1999;28(11):1311-28 18318628 - J Vis. 2008 Feb 15;8(2):2.1-19 8637596 - Nature. 1996 Jun 13;381(6583):607-9 22516647 - IEEE Trans Pattern Anal Mach Intell. 2012 Jun;34(6):1080-91 15621181 - Vision Res. 2005 Mar;45(5):643-59 16469349 - Vision Res. 2006 Jun;46(12):1857-62 |
| References_xml | – ident: CIT0012 publication-title: Visual Cognition – ident: CIT0008 doi: 10.1167/8.2.2 – ident: CIT0031 doi: 10.1109/TPAMI.2009.112 – start-page: 405 volume-title: Proceedings of the 29th annual symposium of the German Association for Pattern Recognition (DAGM 2007) year: 2007 ident: CIT0023 – volume: 4 start-page: 219 issue: 4 year: 1985 ident: CIT0025 publication-title: Human Neurobiology – ident: CIT0053 doi: 10.1016/0042-6989(90)90120-A – ident: CIT0015 doi: 10.3167/proj.2008.020102 – ident: CIT0036 doi: 10.1145/1671954.1671958 – ident: CIT0052 publication-title: Visual Cognition – ident: CIT0054 – ident: CIT0050 – ident: CIT0035 publication-title: Visual Cognition – ident: CIT0030 doi: 10.1016/j.imavis.2010.07.001 – ident: CIT0010 doi: 10.1007/s11263-009-0275-4 – ident: CIT0028 doi: 10.1068/p2935 – ident: CIT0006 doi: 10.1167/10.10.28 – ident: CIT0019 doi: 10.1109/34.730558 – ident: CIT0026 doi: 10.1038/81887 – start-page: 13 volume-title: Attention year: 1998 ident: CIT0051 – ident: CIT0039 doi: 10.1016/j.visres.2005.03.019 – ident: CIT0001 doi: 10.1006/cgip.1993.1033 – ident: CIT0005 – ident: CIT0029 doi: 10.1109/CVPR.2008.4587756 – volume-title: Digital video and HDTV year: 2003 ident: CIT0040 – ident: CIT0045 doi: 10.1167/11.5.5 – ident: CIT0014 doi: 10.1109/TIP.2009.2030969 – ident: CIT0037 doi: 10.1038/381607a0 – volume: 4 start-page: 2379 year: 1987 ident: CIT0011 publication-title: Journal of the Optical Society of America doi: 10.1364/JOSAA.4.002379 – ident: CIT0013 doi: 10.1162/neco.2009.11-06-391 – ident: CIT0016 doi: 10.1109/TIP.2004.834657 – ident: CIT0009 doi: 10.1167/8.14.18 – ident: CIT0027 doi: 10.1016/S0042-6989(01)00102-X – volume: 9 start-page: 175 volume-title: Dynamische Perzeption year: 2000 ident: CIT0034 – volume: 6057 start-page: 116 volume-title: Human vision and electronic imaging XI year: 2006 ident: CIT0002 doi: 10.1117/12.674147 – ident: CIT0043 doi: 10.1016/j.visres.2004.09.017 – ident: CIT0024 doi: 10.1016/j.visres.2005.10.002 – ident: CIT0033 doi: 10.1007/s12559-010-9074-z – volume: 2 start-page: 209 volume-title: Handbook of computer vision and applications year: 1999 ident: CIT0020 – ident: CIT0003 doi: 10.1016/j.visres.2010.08.011 – ident: CIT0055 doi: 10.1167/8.7.32 – volume: 19 start-page: 547 volume-title: Advances in neural information processing systems year: 2006 ident: CIT0018 – ident: CIT0017 doi: 10.1080/13506280444000661 – ident: CIT0032 doi: 10.1109/TPAMI.2006.86 – ident: CIT0007 doi: 10.1167/11.2.20 – ident: CIT0038 doi: 10.1163/15685680360511645 – ident: CIT0046 doi: 10.1080/13506280902764539 – ident: CIT0047 doi: 10.1163/156856809789476065 – ident: CIT0049 – ident: CIT0048 doi: 10.1007/s12559-010-9061-4 – ident: CIT0041 doi: 10.1088/0954-898X/10/4/304 – ident: CIT0004 doi: 10.1167/9.3.5 – ident: CIT0044 doi: 10.1016/j.visres.2005.12.005 – ident: CIT0022 doi: 10.1167/9.5.7 – volume: 2 start-page: 1 issue: 2 year: 2008 ident: CIT0042 publication-title: Journal of Eye Movement Research doi: 10.16910/jemr.2.2.6 – start-page: 2106 volume-title: Proceedings of IEEE international conference on Computer Vision (ICCV) year: 2009 ident: CIT0021 – reference: 19926907 - IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):171-7 – reference: 21367757 - J Vis. 2011 Feb 25;11(2):null – reference: 20801147 - Vision Res. 2010 Oct 28;50(22):2190-9 – reference: 19146264 - J Vis. 2008 Dec 16;8(7):32.1-20 – reference: 10695763 - Network. 1999 Nov;10(4):341-50 – reference: 19709976 - IEEE Trans Image Process. 2010 Jan;19(1):185-98 – reference: 3836989 - Hum Neurobiol. 1985;4(4):219-27 – reference: 21622729 - J Vis. 2011 May 27;11(5):5 – reference: 22516647 - IEEE Trans Pattern Anal Mach Intell. 2012 Jun;34(6):1080-91 – reference: 11100157 - Nat Neurosci. 2000 Dec;3(12):1340-5 – reference: 19757944 - J Vis. 2009 Mar 13;9(3):5.1-24 – reference: 19210172 - Neural Comput. 2009 Jan;21(1):239-71 – reference: 16469349 - Vision Res. 2006 Jun;46(12):1857-62 – reference: 19814903 - Spat Vis. 2009;22(5):397-408 – reference: 15462141 - IEEE Trans Image Process. 2004 Oct;13(10):1304-18 – reference: 2392840 - Vision Res. 1990;30(7):1111-7 – reference: 19757885 - J Vis. 2009 May 13;9(5):7.1-15 – reference: 15621181 - Vision Res. 2005 Mar;45(5):643-59 – reference: 19146319 - J Vis. 2008 Nov 20;8(14):18.1-26 – reference: 15935435 - Vision Res. 2005 Aug;45(18):2397-416 – reference: 16640265 - IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):802-17 – reference: 3430225 - J Opt Soc Am A. 1987 Dec;4(12):2379-94 – reference: 16289663 - Vision Res. 2006 May;46(11):1762-76 – reference: 18318628 - J Vis. 2008 Feb 15;8(2):2.1-19 – reference: 12696858 - Spat Vis. 2003;16(2):125-54 – reference: 8637596 - Nature. 1996 Jun 13;381(6583):607-9 – reference: 20884493 - J Vis. 2010 Aug 26;10(10):28 – reference: 22711998 - Vis cogn. 2012 Jan 1;20(4-5):515-545 – reference: 10755142 - Perception. 1999;28(11):1311-28 – reference: 11718795 - Vision Res. 2001;41(25-26):3559-65 |
| SSID | ssj0016875 |
| Score | 2.0284958 |
| Snippet | We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref informaworld |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 495 |
| SubjectTerms | Algorithms Cognition Data processing Dynamic natural scenes Eye Eye movement variability Intrinsic dimension Learning algorithms Saliency Structure tensor Visual perception |
| Title | Eye movement prediction and variability on natural video data sets |
| URI | https://www.tandfonline.com/doi/abs/10.1080/13506285.2012.667456 https://www.ncbi.nlm.nih.gov/pubmed/22844203 https://www.proquest.com/docview/1257787826 https://www.proquest.com/docview/1826557554 https://pubmed.ncbi.nlm.nih.gov/PMC3404848 https://www.ncbi.nlm.nih.gov/pmc/articles/3404848 |
| UnpaywallVersion | submittedVersion |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1464-0716 dateEnd: 20241103 omitProxy: true ssIdentifier: ssj0016875 issn: 1464-0716 databaseCode: ABDBF dateStart: 19960301 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVLSH databaseName: aylor and Francis Online customDbUrl: mediaType: online eissn: 1464-0716 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016875 issn: 1464-0716 databaseCode: AHDZW dateStart: 19960301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAWR databaseName: Taylor & Francis Social Science and Humanities Library - DRAA customDbUrl: eissn: 1464-0716 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016875 issn: 1464-0716 databaseCode: TRJHH dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.tandfonline.com/ providerName: Taylor & Francis |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwED-N9oG9DNj4CB-TkRBvSZP4M48trJqQmBCi0niKHMcWE21arelQ-euxnQ-1gAZ7TM5W4tzZubN_9zuAN5IpkzEuw6LEaUiojEOpGQ01pkqLUmtuXHLyxwt2PiMfLunlASRdLowH7aviKqrmi6i6-uaxlauFGnU4sREm1uiIuAdDRq37PYDh7OLT-KsPrKgNhVJfhtMuAMQl57AuXU7EIyd3YgfoSiPGOHFlq3d-R3tkpX9zOf9ETt7fVCu5_SHn853f0vQBfO4G1KBRvkebuojUz9-4Hu804odw1DqpaNyIHsGBro7hZFzZAH2xRW-Rh436_fhjOOyX0O0JTM62Gi2WnoO8RqtrdwrkNI9kVaIbG5Y3rOBbZG95SlH7FJcIuEQOqYrWul4_htn07Mu787Ct0hAqkvE6TDNBjcyoMbhIpDA01SlWxmiqCZNCsbgkMlYGE5ExoUodFyQuUqwVp4XEEj-BQbWs9DNAJTdMaJ5o6_WQskgKzKVjkLP9OJZUBIA7ZeWqpTB3lTTmedIynXYqzp2K80bFAYR9r1VD4fGP9mLXDvLab52Yps5Jjm_v-rqzmdxOU3f2Iiu93Kxz60dyuzbaYO6WNlZKrftMSQBPGzvrXzi1bgRJYxwA37PAvoGjCd-XWFvydOGt-QQQ9bb6X9_h-V07vIBDd9Xgml7CoL7e6FfWZauLUxiOJ-8n09N2sv4C1tg6bQ |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jb9QwFH6C9tBeCrQsw2okxC1DEq85FtRqKG0PaCpxi2zHVhHTzKiTKQq_nuds6rAUBNfYL8vLs_09-_NngFdaWJ8JqSNT0DRiXMeRdoJHjnLrVOGc9GFz8smpmJyxo0-8ZxMuO1plyKF9KxTR9NWhcYfJ6J4S9yahvNn6F5hZ6VgIiSjgNmxyxCKYf21OPx5NJsNSglCN2m6wiYJRv3_uN_dZG5_W1Et_hUF_plJurcqFrr_q2ezaOHV4B0z_hS095ct4VZmx_faD-ON_ueAu7HQoluy3YXcPbrlyF_b2S8zgL2rymjS80mbCfhe2hz623oO3B7UjF_NGpLwii8uwTBRCg-CbkCvM21vZ8JrgpUZzFJ8SdgrOSaCykqWrlvfh7PBg-m4Sdcc4RJZlsorSTHGvM-49NYlWnqcupdZ7xx0TWlkRF0zH1lOmMqFs4WLDYpNSZyU3mmr6ADbKeekeASmkF8rJxCEsYoVJDJU6SMyhnaSaqxHQ_uflttM4D0dtzPKkk0Lt_ZYHv-Wt30YQDVaLVuPjD_XV9bjIq2ZuxbcHoeT0ZtOXfQzl2I7D4owu3Xy1zBFoSuw8Mdu7oQ6WcsTXnI3gYRt3wwuniDNYGtMRyLWIHCoEHfH1kvLzeaMnThl24wydNx5i96_88Pjf_fACtibTk-P8-P3phyewHUpaOtRT2KguV-4ZIr3KPO_a8ndme0W9 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB7BVoK-UGihLKeREG9ZkvjMYwtdLQUqhFqpb5bj2GrVbXbVzYLCr8d2Du1yFASvsSfHZGzPeL75DPBSMW0zxlWUFziNCFVxpAyjkcFUG1EYw60vTv54xCYn5PCUnq5U8XtYpY-hbUMUEeZqP7jnhe0Qca8TTEPlnwdmpSPGuHMCbsIG8zmFAWwcfz6cTPpMAhOBbNfLRF6oK5_7zX3Wlqc18tJfuaA_IylvL8u5qr-q6XRlmRpvgeo-sEGnXIyWVT7S337gfvwfDdyFO60Pi_Yao7sHN0y5DTt7pYvfL2v0CgVUadiu34bNfoatd2D_oDbochYoyis0v_JJIm8YyL0I-uKi9oY0vEbuUmAcdU_xdYIz5IGsaGGqxX04GR8cv5lE7SEOkSYZr6I0E9SqjFqL80QJS1OTYm2toYYwJTSLC6JibTERGRO6MHFO4jzFRnOaK6zwAxiUs9I8BFRwy4ThiXFOESnyJMdceYI5J8exomIIuPt3UrcM5_6gjalMWiLUTm_S6002ehtC1EvNG4aPP_QXq2Yhq7CzYptjUCS-XvRFZ0LSjWKfmlGlmS0X0rmZ3E2dLta7po9rpc67pmQIu43Z9S-cOi-DpDEeAl8zyL6DZxFfbynPzwKbOCZuEidOeaPedP9KD4_-XQ_P4dant2P54d3R-8ew6RsaLNQTGFRXS_PUuXlV_qwdyd8BIzxEYQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6V7YFeeLQ8lpeMhLglm8TPHBfUqkKiQoiVyimyHVtU3c2uulmq9NdjOw_tAir0mIytxJmxM2N_8w3AO8m0zRmXkSpxFhEqk0gaRiODqTaiNIZbn5z8-Yydzsinc3q-B2mfCxNA-1pdxNV8EVcXPwK2crXQkx4nNsHEGR0R92CfUed-j2B_dvZl-j0EVtSFQlkow-kWAOKTc1ifLieSiZd7sQd0ZTFjnPiy1Vu_ox2y0r-5nH8iJ-9vqpVsruV8vvVbOnkIX_sBtWiUy3hTq1jf_Mb1eKcRP4IHnZOKpq3oMeyZ6hCOppUL0BcNeo8CbDTsxx_CwbCENkfw4bgxaLEMHOQ1Wl35UyCveSSrEv10YXnLCt4gdytQirqn-ETAJfJIVbQ29foJzE6Ov308jboqDZEmOa-jLBfUypxai1UqhaWZybC21lBDmBSaJSWRibaYiJwJXZpEkURl2GhOlcQSP4VRtazMc0Alt0wYnhrn9ZBSpQpz6RnkXD-OJRVjwL2yCt1RmPtKGvMi7ZhOexUXXsVFq-IxREOvVUvh8Y_2YtsOijpsndi2zkmBb-_6treZwk1Tf_YiK7PcrAvnR3K3Nrpg7pY2Tkqd-0zJGJ61dja8cObcCJIleAx8xwKHBp4mfFfibCnQhXfmM4Z4sNX_-g4v7trhJRz4qxbX9ApG9dXGvHYuW63edJP0FwvwOPs |
| 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=Eye+movement+prediction+and+variability+on+natural+video+data+sets&rft.jtitle=Visual+cognition&rft.au=Dorr%2C+Michael&rft.au=Vig%2C+Eleonora&rft.au=Barth%2C+Erhardt&rft.date=2012-04-01&rft.issn=1350-6285&rft.volume=20&rft.issue=4-5&rft.spage=495&rft.epage=514&rft_id=info:doi/10.1080%2F13506285.2012.667456&rft_id=info%3Apmid%2F22844203&rft.externalDocID=PMC3404848 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1350-6285&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1350-6285&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1350-6285&client=summon |