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Visual time period analysis: a multimedia analytics application for summarizing and analyzing eye-tracking experiments.

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Titel: Visual time period analysis: a multimedia analytics application for summarizing and analyzing eye-tracking experiments.
Autoren: Del Fatto, Vincenzo, Dignös, Anton, Raimato, Guerriero, Maccioni, Lorenzo, Borgianni, Yuri, Gamper, Johann
Quelle: Multimedia Tools & Applications; Dec2019, Vol. 78 Issue 23, p32779-32804, 26p
Schlagwörter: EYE tracking, VISUAL analytics, TIME measurements, ELECTRONIC books, DATA analysis, DIAGNOSTIC imaging
Abstract: Recently, an increasing need for sophisticated multimedia analytics tools has been observed, which is triggered by a rapid growth of multimedia collections and by an increasing number of scientific fields embedding images in their studies. Although temporal data is ubiquitous and crucial in many applications, such tools typically do not support the analysis of data along the temporal dimension, especially for time periods. An appropriate visualization and comparison of period data associated with multimedia collections would help users to infer new information from such collections. In this paper, we present a novel multimedia analytics application for summarizing and analyzing temporal data from eye-tracking experiments. The application combines three different visual approaches: Time∘diff, visual-information-seeking mantra, and multi-viewpoint. A qualitative evaluation with domain experts confirmed that our application helps decision makers to summarize and analyze multimedia collections containing period data. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multimedia Tools & Applications; Dec2019, Vol. 78 Issue 23, p32779-32804, 26p
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  Data: <searchLink fieldCode="DE" term="%22EYE+tracking%22">EYE tracking</searchLink><br /><searchLink fieldCode="DE" term="%22VISUAL+analytics%22">VISUAL analytics</searchLink><br /><searchLink fieldCode="DE" term="%22TIME+measurements%22">TIME measurements</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTRONIC+books%22">ELECTRONIC books</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+analysis%22">DATA analysis</searchLink><br /><searchLink fieldCode="DE" term="%22DIAGNOSTIC+imaging%22">DIAGNOSTIC imaging</searchLink>
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  Data: Recently, an increasing need for sophisticated multimedia analytics tools has been observed, which is triggered by a rapid growth of multimedia collections and by an increasing number of scientific fields embedding images in their studies. Although temporal data is ubiquitous and crucial in many applications, such tools typically do not support the analysis of data along the temporal dimension, especially for time periods. An appropriate visualization and comparison of period data associated with multimedia collections would help users to infer new information from such collections. In this paper, we present a novel multimedia analytics application for summarizing and analyzing temporal data from eye-tracking experiments. The application combines three different visual approaches: Time<superscript>∘</superscript>diff, visual-information-seeking mantra, and multi-viewpoint. A qualitative evaluation with domain experts confirmed that our application helps decision makers to summarize and analyze multimedia collections containing period data. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-019-07950-1
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        Text: English
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              Text: Dec2019
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