Performance Evaluation in Multimedia Retrieval
Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for the retrieval process itself and the subsequent verification of results. Such experiments can be elaborate and use-case-specific, which can make them difficult to compare or replicate. In this article, we present a formal model to express all relevant aspects of such retrieval experiments, as well as a flexible open-source evaluation infrastructure that implements the model. These contributions intend to make a step towards lowering the hurdles for conducting retrieval experiments and improving their reproducibility.
Authors
- Ralph Gasser (ORCID: https://orcid.org/0000-0002-3016-1396)
- Heiko Schuldt (ORCID: https://orcid.org/0000-0001-9865-6371)
- Luca Rossetto (ORCID: https://orcid.org/0000-0002-5389-9465)
- Loris Sauter (ORCID: https://orcid.org/0000-0001-8046-0362)
- Abraham Bernstein (ORCID: https://orcid.org/0000-0002-0128-4602)
Institutions
- University of Basel (CH)
- University of Zurich (CH)
Publication Details
- Journal
- ACM Transactions on Multimedia Computing Communications and Applications
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1145/3678881
- Citations
- 13
- Primary Topic
- Advanced Image and Video Retrieval Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.24
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung