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Aging contrast: A contrastive learning framework for fish re-identification across seasons and years.

November 27, 2023

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.

Publication Year 2023
Title Aging contrast: A contrastive learning framework for fish re-identification across seasons and years.
DOI 10.1007/978-981-99-8388-9_21
Authors Weili Shi, Z. Zhou, Benjamin Letcher, Nathaniel Hitt, Yoichiro Kanno, R. Futamura, O. Kishida, K. Morita, Sheng Li
Publication Type Conference Paper
Publication Subtype Conference Paper
Index ID 70243997
Record Source USGS Publications Warehouse
USGS Organization Eastern Ecological Science Center