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.

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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

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article

Performance Evaluation in Multimedia Retrieval

Ralph Gasser, Heiko Schuldt, Luca Rossetto, Loris Sauter et al.
13 citations
ACM Transactions on Multimedia Computing Communications and Applications
Advanced Image and Video Retrieval Techniques
3.24
article

Performance Evaluation in Multimedia Retrieval

Ralph Gasser, Heiko Schuldt, Luca Rossetto, Loris Sauter, Abraham Bernstein
article en
13 citations

Abstract

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.

ACM Transactions on Multimedia Computing Communications and ApplicationsVol. 21(1)
University of Basel (CH), University of Zurich (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Advanced Image and Video Retrieval Techniques
3.24
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