Measuring user performance during interactions with digital video collections | Zendy

Yang Meng | Zendy; Wildemuth Barbara M. | Zendy; Marchionini Gary | Zendy; Wilkens Todd | Zendy; Geisler Gary | Zendy; Hughes Anthony | Zendy; Gruss Richard | Zendy; Webster Curtis | Zendy

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Measuring user performance during interactions with digital video collections

Author(s) -

Yang Meng,

Wildemuth Barbara M.,

Marchionini Gary,

Wilkens Todd,

Geisler Gary,

Hughes Anthony,

Gruss Richard,

Webster Curtis

Publication year - 2003

Publication title -

proceedings of the american society for information science and technology

Language(s) - English

Resource type - Journals

eISSN - 1550-8390

pISSN - 0044-7870

DOI - 10.1002/meet.1450400101

Subject(s) - computer science , inference , digital video , action recognition , object (grammar) , information retrieval , gist , multimedia , human–computer interaction , artificial intelligence , frame (networking) , class (philosophy) , medicine , telecommunications , stromal cell , pathology

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.

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