This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu).
Table of Links
- Abstract and Intro
- Related Wok
- VEATIC Dataset
- Experiments
- Discussion
- Conclusion
- More About Stimuli
- Annotation Details
- Outlier Processing
- Subject Agreement Across Videos
- Familiarity and Enjoyment Ratings and References
11. Familiarity and Enjoyment Ratings
Familiarity and enjoyment ratings were collected for each video across participants, as shown in Figure 13. Familiarity and enjoyment ratings for video IDs 0-83 were collected in a scale of 1-5 and 1-9, respectively. Familiarity and enjoyment ratings for video IDs 83-123 were collected prior to the planning of the VEATIC dataset and were collected on a different scale. Familiarity and enjoyment ratings for video IDs 83-97 were collected on a scale of 0- 5 and familiarity/enjoyment ratings were not collected for video IDs 98-123. For analysis and visualization purposes, we rescaled the familiarity and enjoyment ratings for video IDs 83-97 to 1-5 and 1-9, respectively, to match video IDs 0-83. To rescale the familiarity values from 0-5 to 1-5 we performed a linear transformation, we first normalized the data between 0 and 1, then we multiplied the values by 4 and added 1. We rescaled the enjoyment values from 0-5 to 1-9 similarly by first normalizing the data between 0 and 1, then we multiplied the values by 8 and added 1. As a result, the average familiarity rating was 1.61 while the average enjoyment rating was 4.98 for video IDs 0-97.
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This paper is available on arxiv under CC 4.0 license.