Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning
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[1] Jiri Matas,et al. Systematic evaluation of convolution neural network advances on the Imagenet , 2017, Comput. Vis. Image Underst..
[2] Qiang Chen,et al. Network In Network , 2013, ICLR.
[3] R. Bouckaert,et al. bModelTest: Bayesian phylogenetic site model averaging and model comparison , 2015, BMC Evolutionary Biology.
[4] Kevin J. Gaston,et al. Image analysis, neural networks, and the taxonomic impediment to biodiversity studies , 1997, Biodiversity & Conservation.
[5] Kevin J. Gaston,et al. Automating insect identification: exploring the limitations of a prototype system , 1999 .
[6] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[7] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[8] I. Oliver,et al. Invertebrate Morphospecies as Surrogates for Species: A Case Study , 1996 .
[9] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[10] Vicki A. Funk,et al. Applications of deep convolutional neural networks to digitized natural history collections , 2017, Biodiversity data journal.
[11] Patrick Mäder,et al. Automated plant species identification—Trends and future directions , 2018, PLoS Comput. Biol..
[12] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[13] Anthony D. Griffiths,et al. Using ants as bioindicators in land management: simplifying assessment of ant community responses , 2002 .
[14] A. Andersen. Using Ants as bioindicators: Multiscale Issues in Ant Community Ecology , 1997 .
[15] Rutger A. Vos,et al. OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks , 2016, bioRxiv.
[16] David Coderre,et al. Understanding data , 2020, An SPSS Guide for Tourism, Hospitality and Events Researchers.
[17] M. Kaspari,et al. A life history continuum in the males of a Neotropical ant assemblage: refuting the sperm vessel hypothesis , 2012, Naturwissenschaften.
[18] J. Orivel,et al. A new method based on taxonomic sufficiency to simplify studies on Neotropical ant assemblages , 2010 .
[19] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[20] Silvio Savarese,et al. Robust single-view instance recognition , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[21] Henrik Skov Midtiby,et al. Plant species classification using deep convolutional neural network , 2016 .
[22] I. Oliver,et al. Taxonomic sufficiency in ecological studies of terrestrial invertebrates , 1999 .
[23] Mark A. O'Neill,et al. Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY) , 2004 .
[24] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[25] Timothy Dozat,et al. Incorporating Nesterov Momentum into Adam , 2016 .
[26] R. Levy. IDENTIFICATION GUIDE TO THE ANT GENERA OF THE WORLD , 1995 .
[27] Changshui Zhang,et al. DeepFish: Accurate underwater live fish recognition with a deep architecture , 2016, Neurocomputing.
[28] Marcel Salathé,et al. Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..
[29] André Carlos Ponce de Leon Ferreira de Carvalho,et al. Deep learning for biological image classification , 2017, Expert Syst. Appl..
[30] Qingbin Zhan,et al. A tool for developing an automatic insect identification system based on wing outlines , 2015, Scientific Reports.
[31] Kevin J. Gaston,et al. Automating the identification of insects: a new solution to an old problem , 1997 .
[32] Paolo Remagnino,et al. Deep-plant: Plant identification with convolutional neural networks , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[33] Martin Drauschke,et al. Identification of Africanized honey bees through wing morphometrics: two fast and efficient procedures , 2008, Apidologie.
[34] J. Biesmeijer,et al. Discrimination of haploid and diploid males of Bombus terrestris (Hymenoptera; Apidae) based on wing shape , 2015, Apidologie.
[35] Ben C. Stöver,et al. LeafNet: A computer vision system for automatic plant species identification , 2017, Ecol. Informatics.
[36] Yu Sun,et al. Deep Learning for Plant Identification in Natural Environment , 2017, Comput. Intell. Neurosci..
[37] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[38] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[39] Gaines E. Miles,et al. MACHINE VISION AND IMAGE PROCESSING FOR PLANT IDENTIFICATION. , 1986 .
[40] Paolo Remagnino,et al. Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification Task , 2016, CLEF.
[41] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[42] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[43] Liqiang Ji,et al. The identification of butterfly families using content-based image retrieval , 2012 .
[44] Jiangning Wang,et al. A new automatic identification system of insect images at the order level , 2012, Knowl. Based Syst..
[45] Chenglu Wen,et al. Image-based orchard insect automated identification and classification method , 2012 .
[46] Jeremy A. Miller,et al. A quantitative assessment of the vegetation types on the island of St. Eustatius, Dutch Caribbean , 2016 .
[47] Mark D. McDonnell,et al. Understanding Data Augmentation for Classification: When to Warp? , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[48] Seung-Ho Kang,et al. Identification of butterfly species with a single neural network system , 2012 .
[49] Shiliang Sun,et al. Multi-view learning overview: Recent progress and new challenges , 2017, Inf. Fusion.
[50] M. O'Neill,et al. Automated species identification: why not? , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[51] P. Bonnet,et al. Going deeper in the automated identification of Herbarium specimens , 2017, BMC Evolutionary Biology.
[52] M. Edwards,et al. The potential for computer-aided identification in biodiversity research. , 1995, Trends in ecology & evolution.
[53] Gary Marcus,et al. Deep Learning: A Critical Appraisal , 2018, ArXiv.
[54] S. Ferrier,et al. Biogeographical concordance and efficiency of taxon indicators for establishing conservation priority in a tropical rainforest biota , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[55] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[56] C. Grey,et al. Mouse PRDM9 DNA-Binding Specificity Determines Sites of Histone H3 Lysine 4 Trimethylation for Initiation of Meiotic Recombination , 2011, PLoS biology.
[57] I. Guarniero. How Many Species Are There on Earth and in the Ocean? (PLOS Biology) , 2014 .
[58] Silvio Savarese,et al. Deep Learning for Single-View Instance Recognition , 2015, ArXiv.
[59] P. S. Ward. Phylogeny, classification, and species-level taxonomy of ants (Hymenoptera: Formicidae)* , 2007 .
[60] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[61] D. Ellis. Taxonomic sufficiency in pollution assessment , 1985 .
[62] B. Bolton. Book Review Identification Guide to the Ant Genera of the World , 1994 .