Abstract
While AI-based audiovisual analysis tools have without doubt made huge progress, integrating them in media production and archiving workflows is still challenging, as the provided annotations may not match needs in terms of type, granularity and accuracy of metadata, and do not well align with existing workflows. We propose a system for annotation and search in media archive applications, using a range of AI-based analysis methods. In order to facilitate communication of explanations and collect relevance feedback, an impersonation of the systems’ intelligence, named Taylor, is included as an element of the user interface.
This work has received funding from the program “ICT of the Future” of the Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) in the project “TailoredMedia”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bailer, W., Fassold, H.: People@Places and ToDY: two datasets for scene classification in media production and archiving. In: International Conference on MM Modeling (2023)
Ciccarese, P., Soiland-Reyes, S., Clark, T.: Web annotation as a first-class object. IEEE Internet Comput. 17(6), 71–75 (2013)
Cooper, A., Reimann, R., Cronin, D., Noessel, C.: About Face: The Essentials of Interaction Design. John Wiley, Hoboken (2014)
Dan, A.: ARD-Normdatenbank - Nutzung und Pflege von Normdaten in der ARD. IN2N-Workshop. http://in2n.de/medien/2014/08/20140930_IN2N-Workshop_Dan_ARDNormdatenbank.pdf (2014)
Fassold, H., Ghermi, R.: Omnitrack: real-time detection and tracking of objects, text and logos in video. In: IEEE International Symposium on Multimedia (ISM) (2019)
de Jesus Oliveira, V.A., Rottermanner, G., Größbacher, S., Boucher, M., Judmaier, P.: Requirements and concepts for interactive media retrieval user interfaces. In: Nordic Human-Computer Interaction Conference. NordiCHI 2022 (2022)
Winter, M., Bailer, W.: Incremental training for face recognition. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., Vrochidis, S. (eds.) MMM 2019. LNCS, vol. 11295, pp. 289–299. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05710-7_24
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
de Jesus Oliveira, V.A. et al. (2023). Taylor – Impersonation of AI for Audiovisual Content Documentation and Search. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_63
Download citation
DOI: https://doi.org/10.1007/978-3-031-27818-1_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27817-4
Online ISBN: 978-3-031-27818-1
eBook Packages: Computer ScienceComputer Science (R0)