Abstract
The fields of biology, ecology, and fisheries management are witnessing a growing demand for distinguishing individual fish. In recent years, deep learning methods have emerged as a promising tool for image-based fish recognition. Our study is focused on the re-identification of masu salmon from Japan, wherein fish were individually marked and photographed to evaluate discriminative body characteristics. Unlike previous studies where fish were sampled during the same time period, we evaluated individual re-identification across seasons and years to address challenges due to aging, seasonal variation, and other factors. In this paper, we propose a new contrastive learning framework called Aging Contrast (AgCo) and evaluate its performance on the masu salmon dataset. Our analysis indicates that, unlike large changes in body size over time, the pattern of parr marks on the lateral line of the fish body remains relatively stable, despite some change in coloration across seasons. AgCo accounts for such seasonally-invariant features and performs re-identification based on the cosine similarity of these features. Extensive experiments show that our AgCo method outperforms other state-of-the-art methods.
W. Shi and Z. Zhou—Contributed equally to this work.
The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey.
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This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G22AC00372.
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Shi, W. et al. (2024). Aging Contrast: A Contrastive Learning Framework for Fish Re-identification Across Seasons and Years. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_21
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