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Quantifying plant mimesis in fossil insects using deep learning
Fan, Li1; Xu, Chunpeng(徐春鹏)2,3,4; Jarzembowski, Edmund A.2,3; Cui, Xiaohui1
2021-07-15
Source PublicationHISTORICAL BIOLOGY
ISSN0891-2963
Pages10
Abstract

As an important combination of behaviour and pattern in animals to resemble benign objects, biolog ical mimesis can effectively avoid the detection of their prey and predators. It at least dates back to the Permian in fossil records. The recognition of mimesis within fossil, however, might be subjective and lack quantitative analysis being only based on few fossils with limited information. To compensate for this omission, we propose a new method using a Siamese network to measure the dissimilarity between hypothetical mimics and their models from images. It generates dissimilarity values between paired images of organisms by extracting feature vectors and calculating Euclidean distances. Additionally, the idea of 'transfer learning' is adopted to fine-tune the Siamese network, to overcome the limitations of available fossil image pairs. We use the processed Totally-Looks-Like, a large similar image data set, to pretrain the Siamese network and fine-tune it with a collected mimetic-image data set. Based on our results, we propose two recommended image dissimilarity thresholds for judging the mimicry of extant insects (0-0.4556) and fossil insects (0-0.4717). Deep learning algorithms are used to quantify the mimicry of fossil insects in this study, providing novel insights into exploring the early evolution of mimicry.

KeywordMimesis fossil insects similarity deep learning Siamese network
DOI10.1080/08912963.2021.1952199
Indexed BySCI
Language英语
WOS KeywordCOLOR PATTERNS ; MIMICRY
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDB26000000] ; Second Tibetan Plateau Scientific Expedition and Research[2019QZKK0706] ; National Natural Science Foundation of China[41688103] ; Chinese Academy of Sciences
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Paleontology
WOS SubjectBiology ; Paleontology
WOS IDWOS:000673247500001
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences ; Second Tibetan Plateau Scientific Expedition and Research ; National Natural Science Foundation of China ; Chinese Academy of Sciences
PublisherTAYLOR & FRANCIS LTD
Document Type期刊论文
Identifierhttp://ir.nigpas.ac.cn/handle/332004/38421
Collection中国科学院南京地质古生物研究所
Corresponding AuthorCui, Xiaohui
Affiliation1.Wuhan Univ, Sch Cyber Sci & Engn, Minist Educ, Key Lab Aerosp Informat Secur & Trusted Comp, Wuhan, Peoples R China
2.Chinese Acad Sci, Nanjing Inst Geol & Palaeontol, State Key Lab Palaeobiol & Stratig, Nanjing, Peoples R China
3.Univ Chinese Acad Sci, Ctr Excellence Life & Paleoenvironm, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Fan, Li,Xu, Chunpeng,Jarzembowski, Edmund A.,et al. Quantifying plant mimesis in fossil insects using deep learning[J]. HISTORICAL BIOLOGY,2021:10.
APA Fan, Li,Xu, Chunpeng,Jarzembowski, Edmund A.,&Cui, Xiaohui.(2021).Quantifying plant mimesis in fossil insects using deep learning.HISTORICAL BIOLOGY,10.
MLA Fan, Li,et al."Quantifying plant mimesis in fossil insects using deep learning".HISTORICAL BIOLOGY (2021):10.
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