Open-set plant identification using an ensemble of deep convolutional neural networks

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Official URL: http://ceur-ws.org/Vol-1609/

Open-set recognition, a challenging problem in computer vision, is concerned with identification or verification tasks where queries may belong to unknown classes. This work describes a fine-grained plant identification system consisting of an ensemble of deep convolutional neural networks within an open-set identification framework. Two wellknown deep learning architectures of VGGNet and GoogLeNet, pretrained on the object recognition dataset of ILSVRC 2012, are finetuned using the plant dataset of LifeCLEF 2015. Moreover, GoogLeNet is fine-tuned using plant and non-plant images for rejecting samples from non-plant classes. Our systems have been evaluated on the test dataset of PlantCLEF 2016 by the campaign organizers and our best proposed model has achieved an official score of 0.738 in terms of the mean average precision, while the best official score is 0.742.