Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data

• URL: http://arxiv.org/abs/2311.08661v1
• Date: Wed, 15 Nov 2023 02:57:59 GMT
• Title: Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data
• Authors: Li Xu, Yili Hong, Eric P. Smith, David S. McLeod, Xinwei Deng, Laura J. Freeman
• Abstract summary: Deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained. We demonstrate that the algorithm can successfully classify an image into a new class if the image does not belong to the existing classes.
• Score: 5.943822554753426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As is true of many complex tasks, the work of discovering, describing, and understanding the diversity of life on Earth (viz., biological systematics and taxonomy) requires many tools. Some of this work can be accomplished as it has been done in the past, but some aspects present us with challenges which traditional knowledge and tools cannot adequately resolve. One such challenge is presented by species complexes in which the morphological similarities among the group members make it difficult to reliably identify known species and detect new ones. We address this challenge by developing new tools using the principles of machine learning to resolve two specific questions related to species complexes. The first question is formulated as a classification problem in statistics and machine learning and the second question is an out-of-distribution (OOD) detection problem. We apply these tools to a species complex comprising Southeast Asian stream frogs (Limnonectes kuhlii complex) and employ a morphological character (hind limb skin texture) traditionally treated qualitatively in a quantitative and objective manner. We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained. We further demonstrate that the algorithm can successfully classify an image into a new class if the image does not belong to the existing classes. Additionally, we use the larger MNIST dataset to test the performance of our OOD detection algorithm. We finish our paper with some concluding remarks regarding the application of these methods to species complexes and our efforts to document true biodiversity. This paper has online supplementary materials.

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