Plant identification with deep learning ensembles in ExpertLifeCLEF 2018 - Ahmed, Sara Atito Ali; Yanikoglu, Berrin A.; Aptoula, Erchan; Ganiyusufolu, Ipek; Yildi, Aras; Yıldırır, Kerem; Sevilmiş, Barış (CEUR-WS ceurws@sunsite.informatik.rwth-aachen.de, 2018)
This work describes the plant identification system that we submitted to the ExpertLifeCLEF plant identification campaign in 2018. We fine-tuned two pre-trained deep learning architectures (SeNet and DensNetwork) using images shared by the CLEF organizers in 2017. Our main runs are 4 ensembles obtained with different weighted combinations of the 4 deep learning architectures. The fifth ensemble is based on deep learning features but uses Error Correcting Output Codes (ECOC) as the ensemble. Our best system has achieved a classification accuracy of 74.4%, while the best system obtained 86.7% accuracy, on the whole of the official test data. This system ranked 4th place among all the teams, but matched the accuracy of one of the human experts. © 2018 Elsevier B.V., All rights reserved.
19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018 -- Avignon -- 138100
Keywords: Convolutional neural networks; Deep learning; Plant identification