Abstract
The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control.
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Notes
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We can also view this as an extreme form of input augmentation.
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Acknowledgements
We would like to acknowledge financial support from the MBIE Endeavour research grant “Biosecure-ID”. We would like to thank Dr. Michael Cree for many useful discussions and Dr. Jerry Cooper and Dr. Aaron Wilton as the experts behind NatureWatch for their contribution.
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Mo, J., Frank, E., Vetrova, V. (2017). Large-Scale Automatic Species Identification. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_24
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DOI: https://doi.org/10.1007/978-3-319-63004-5_24
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