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Requirements and Concepts for Interactive Media Retrieval User Interfaces

Published: 08 October 2022 Publication History

Abstract

Large amounts of multimedia data are constantly digitized and stored in archives. Accurate search and annotation tools are essential for the fast retrieval of archival records by archivists, scientists, and the general public. The complexity of processing and navigating large collections evidence the demand for solutions that are tailored to the needs of diverse target groups. In this paper, we investigate the requirements for multimedia search and annotation tools. After identifying examples of graphical user interfaces and visualization techniques to support navigating and annotating audiovisual content in archives, we performed an iterative user research. Based on expert interviews, focus groups, and surveys, we propose a series of requirements and concept ideas for user interfaces aimed at quality control and AI-assisted search of multimedia data. Results also show that open challenges and needs include the definition of tailored ontologies to describe archival multimedia data.

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Cited By

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  • (2023)Who’s in My Archive? An End-to-End Framework for Automatic Annotation of TV Personalities2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386323(2061-2070)Online publication date: 15-Dec-2023
  • (2023)Taylor – Impersonation of AI for Audiovisual Content Documentation and SearchMultiMedia Modeling10.1007/978-3-031-27818-1_63(751-757)Online publication date: 31-Mar-2023

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Published In

cover image ACM Other conferences
NordiCHI '22: Nordic Human-Computer Interaction Conference
October 2022
1091 pages
ISBN:9781450396998
DOI:10.1145/3546155
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2022

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Author Tags

  1. human-in-the-loop AI
  2. media retrieval interfaces
  3. user research

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  • Research-article
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  • Refereed limited

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  • Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK)

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NordiCHI '22

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Overall Acceptance Rate 379 of 1,572 submissions, 24%

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View all
  • (2023)Who’s in My Archive? An End-to-End Framework for Automatic Annotation of TV Personalities2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386323(2061-2070)Online publication date: 15-Dec-2023
  • (2023)Taylor – Impersonation of AI for Audiovisual Content Documentation and SearchMultiMedia Modeling10.1007/978-3-031-27818-1_63(751-757)Online publication date: 31-Mar-2023

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