Identifying experts in the field of visual arts using oculomotor signals
In this article, we aimed to present a system that enables identifying experts in the field of visual art based on oculographic data. The difference between the two classified groups of tested people concerns formal education. At first, regions of interest (ROI) were determined based on position of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Journal of Eye Movement Research |
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| Online Access: | https://bop.unibe.ch/JEMR/article/view/4185 |
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| _version_ | 1849764465157865472 |
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| author | Marcin Kolodziej Andrzej Majkowski Piotr Francuz Remigiusz J. Rak Paweł Augustynowicz |
| author_facet | Marcin Kolodziej Andrzej Majkowski Piotr Francuz Remigiusz J. Rak Paweł Augustynowicz |
| author_sort | Marcin Kolodziej |
| collection | DOAJ |
| description | In this article, we aimed to present a system that enables identifying experts in the field of visual art based on oculographic data. The difference between the two classified groups of tested people concerns formal education. At first, regions of interest (ROI) were determined based on position of fixations on the viewed picture. For each ROI, a set of features (the number of fixations and their durations) was calculated that enabled distinguishing professionals from laymen. The developed system was tested for several dozen of users. We used k-nearest neighbors (k-NN) and support vector machine (SVM) classifiers for classification process. Classification results proved that it is possible to distinguish experts from non-experts. |
| format | Article |
| id | doaj-art-bee61f96ceaf4d62bc1f87c8aa6e4520 |
| institution | DOAJ |
| issn | 1995-8692 |
| language | English |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-bee61f96ceaf4d62bc1f87c8aa6e45202025-08-20T03:05:07ZengMDPI AGJournal of Eye Movement Research1995-86922018-05-0111310.16910/jemr.11.3.3Identifying experts in the field of visual arts using oculomotor signalsMarcin Kolodziej0Andrzej Majkowski1Piotr Francuz2Remigiusz J. Rak3Paweł Augustynowicz4Warsw Univerity of TechnologyWarsaw University of TechnologyJohn Paul II Catholic University of LublinWarsaw University of TechnologyJohn Paul II Catholic University of LublinIn this article, we aimed to present a system that enables identifying experts in the field of visual art based on oculographic data. The difference between the two classified groups of tested people concerns formal education. At first, regions of interest (ROI) were determined based on position of fixations on the viewed picture. For each ROI, a set of features (the number of fixations and their durations) was calculated that enabled distinguishing professionals from laymen. The developed system was tested for several dozen of users. We used k-nearest neighbors (k-NN) and support vector machine (SVM) classifiers for classification process. Classification results proved that it is possible to distinguish experts from non-experts.https://bop.unibe.ch/JEMR/article/view/4185Expert systemeye-trackingfixationclustersneural networksupport vector machine |
| spellingShingle | Marcin Kolodziej Andrzej Majkowski Piotr Francuz Remigiusz J. Rak Paweł Augustynowicz Identifying experts in the field of visual arts using oculomotor signals Journal of Eye Movement Research Expert system eye-tracking fixation clusters neural network support vector machine |
| title | Identifying experts in the field of visual arts using oculomotor signals |
| title_full | Identifying experts in the field of visual arts using oculomotor signals |
| title_fullStr | Identifying experts in the field of visual arts using oculomotor signals |
| title_full_unstemmed | Identifying experts in the field of visual arts using oculomotor signals |
| title_short | Identifying experts in the field of visual arts using oculomotor signals |
| title_sort | identifying experts in the field of visual arts using oculomotor signals |
| topic | Expert system eye-tracking fixation clusters neural network support vector machine |
| url | https://bop.unibe.ch/JEMR/article/view/4185 |
| work_keys_str_mv | AT marcinkolodziej identifyingexpertsinthefieldofvisualartsusingoculomotorsignals AT andrzejmajkowski identifyingexpertsinthefieldofvisualartsusingoculomotorsignals AT piotrfrancuz identifyingexpertsinthefieldofvisualartsusingoculomotorsignals AT remigiuszjrak identifyingexpertsinthefieldofvisualartsusingoculomotorsignals AT pawełaugustynowicz identifyingexpertsinthefieldofvisualartsusingoculomotorsignals |