Ant genera identification using an ensemble of convolutional neural networks

Works requiring taxonomic knowledge face several challenges, such as arduous identification of many taxa and an insufficient number of taxonomists to identify a great deal of collected organisms. Machine learning tools, particularly convolutional neural networks (CNNs), are then welcome to automatically generate high-performance classifiers from available data. Supported by the image datasets available at the largest online database on ant biology, the AntWeb (www.antweb.org), we propose here an ensemble of CNNs to identify ant genera directly from the head, profile and dorsal perspectives of ant images. Transfer learning is also considered to improve the individual performance of the CNN classifiers. The performance achieved by the classifiers is diverse enough to promote a reduction in the overall classification error when they are combined in an ensemble, achieving an accuracy rate of over 80% on top-1 classification and an accuracy of over 90% on top-3 classification131CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP141308/2014-1; 131488/2015-5; 311751/2013-0; 309115/2014-023038.002884/2013-382014/13533-

Ant genera identification using an ensemble of convolutional neural networks.

Works requiring taxonomic knowledge face several challenges, such as arduous identification of many taxa and an insufficient number of taxonomists to identify a great deal of collected organisms. Machine learning tools, particularly convolutional neural networks (CNNs), are then welcome to automatically generate high-performance classifiers from available data. Supported by the image datasets available at the largest online database on ant biology, the AntWeb (www.antweb.org), we propose here an ensemble of CNNs to identify ant genera directly from the head, profile and dorsal perspectives of ant images. Transfer learning is also considered to improve the individual performance of the CNN classifiers. The performance achieved by the classifiers is diverse enough to promote a reduction in the overall classification error when they are combined in an ensemble, achieving an accuracy rate of over 80% on top-1 classification and an accuracy of over 90% on top-3 classification

Summary of dataset characteristics.

<p>The columns show, respectively: the cardinality constraint; the number of specimens satisfying the constraint; and the rate of the data satisfying the constraint (first row determines the whole set). <i>N</i>_<i>h</i>, <i>N</i>_<i>d</i> and <i>N</i>_<i>p</i> are, respectively, the cardinality of pictures from head, dorsum and profile perspective for each specimen.</p

Classification performance of the SVM with linear and rbf kernel, when the features are extracted from the penultimate layer of an AlexNet CNN trained with an www.image-net.org dataset.

<p>Rows show the performance of each learning machine (SVM with linear kernel and SVM with rbf kernel) on each image view (head, dorsum and profile). Columns show accuracy, average precision and minimum precision performance for each label on top lists. H = head view; D = dorsal view; P = profile view; SVM-L = SVM with linear kernel; SVM-R = SVM with rbf kernel.</p

Diversity performance on sets of learning machines on validation set.

<p>G = general classifier; S = specific classifier; T = transfer classifier.</p

Example of head , profile , and dorsal image perspectives of an <i>Odontomachus chelifer</i> specimen.

<p>Ant specimen photographs by April Nobile. Taken from <a href="http://www.antweb.org" target="_blank">www.antweb.org</a>.</p

Representation of the ensemble exploring different types of image views.

<p>Ant specimen photographs by April Nobile, taken from <a href="http://www.antweb.org" target="_blank">www.antweb.org</a>.</p

Operation in a convolutional layer with ReLU and Pooling.

<p>The first column shows a (6 × 6) input example with 3 channels. The columns from second to forth show the interaction of this input with a receptive field (3 × 3, represented in the third column) generating a (6 × 6) output with 1 channel in the forth column. The second column shows a padding with value 1, and the applied stride is also 1. The fifth column represents the output of a ReLU operation. The last column presents the 3 × 3 output for a 2 × 2 window Max Pooling operation (represented in the sixth column).</p

Number of ant genera and species with at least <i>n</i> specimens.

<p>Number of ant genera and species with at least <i>n</i> specimens.</p

Classification performance of ensembles and CNN learning machines in the test set.

<p>The best ensemble and single view classifiers for each performance metric are highlighted in bold and blue, respectively. H = head view; D = dorsal view; P = profile view; G = general classifier; S = specific classifier; T = transfer classifier; E = ensemble classifier.</p