Measuring User Performance During Interactions with Digital Video Collections

机译：在与数字视频馆藏互动期间衡量用户性能

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With more and more digital videos found online, video retrieval researchers have begun to create various representations or surrogates for digital videos, such as poster frames, storyboards, video skims and fast forwards. How to evaluate the effectiveness of these video surrogates has become an issue for researchers. This paper proposes two general classes of user tasks―recognition tasks and tasks requiring inference―for which performance measures were developed. The measures include graphical object recognition, textual object recognition, action recognition, free-text gist determination, multiple-choice gist determination and visual gist determination. The preliminary results from two user studies applying these six measures are also discussed in this paper.

机译：随着在线上发现越来越多的数字视频，视频检索研究人员已开始为数字视频创建各种表示形式或替代形式，例如海报框架，情节提要，视频剪辑和快进。如何评估这些视频替代产品的有效性已成为研究人员的问题。本文提出了两类通用的用户任务，即识别任务和需要推理的任务，针对这些任务开发了性能指标。这些措施包括图形对象识别，文本对象识别，动作识别，自由文本要点确定，多项选择要点确定和视觉要点确定。本文还讨论了使用这六种方法进行的两次用户研究的初步结果。

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来源
《66th ASIST (American Society for Information Science and Technology) Annual Meeting; Oct 19-22, 2003; Long Beach, CA》|2003年|p.3-11|共9页
会议地点 Long Beach CA(US);Long Beach CA(US)
作者
Meng Yang; Barbara M. Wildemuth; Gary Marchionini; Todd Wilkens; Gary Geisler; Anthony Hughes; Richard Gruss; Curtis Webster;
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作者单位

Open Video Project, Interaction Design Lab, School of Information and Library Science, CB #3360, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3360;

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原文格式 PDF
正文语种 eng
中图分类信息与知识传播;
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Measuring User Performance During Interactions with Digital Video Collections

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