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.
Published at: ExpertLifeCLEF 2018
Link to paper: CEUR-WS Vol-2125