Aging contrast: A contrastive learning framework for fish re-identification across seasons and years.
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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.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Aging contrast: A contrastive learning framework for fish re-identification across seasons and years. |
| DOI | 10.1007/978-981-99-8388-9_21 |
| Volume | 14471 |
| Year Published | 2023 |
| Language | English |
| Publisher | Springer |
| Contributing office(s) | Eastern Ecological Science Center |
| Description | 13 p. |
| First page | 252 |
| Last page | 264 |
| Conference Title | AI 2023: Advances in Artificial Intelligence: 36th Australasian Joint Conference on Artificial Intelligence, AI 2023 |
| Conference Location | Brisbane, Australia |
| Conference Date | November 28-December 1, 2023 |
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