Abstract
Thanks to the availability of large digital collections of coral images and because of the difficulty for experts to manually process all of them, it is possible and valuable to apply automatic methods to identify similar and relevant coral specimens in a coral specimen collection. Given the digital nature of these collections, it makes sense to leverage computer vision and information retrieval methods to support marine biology experts with their research.
In this paper we introduce CoralExp: a data exploration system aimed at supporting domain experts in marine biology by means of explainable computer vision and machine learning techniques in better understanding the reasoning behind automated classification decisions and thus providing insights on which coral properties should to be considered when designing future coral taxonomies.
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Acknowledgements
This work is partially supported by the ARC Discovery Project (Grant No. DP190102141).
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Harding, J., Bridge, T., Demartini, G. (2021). CoralExp: An Explainable System to Support Coral Taxonomy Research. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_55
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DOI: https://doi.org/10.1007/978-3-030-72240-1_55
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