Benchmark database for fine-grained image classification of benthic macroinvertebrates

作者：

Highlights：

• We publish a database with 64 types of freshwater macroinvertebrates and more than 15,000 images.

• CNNs outperforms a non-expert human in classification accuracy.

• Several other well-known classifiers are applied using the features extracted from the CNNs.

• In most cases, the classifiers cannot further improve the CNN results.

摘要

•We publish a database with 64 types of freshwater macroinvertebrates and more than 15,000 images.•CNNs outperforms a non-expert human in classification accuracy.•Several other well-known classifiers are applied using the features extracted from the CNNs.•In most cases, the classifiers cannot further improve the CNN results.

论文关键词：Biomonitoring,Fine-grained classification,Benthic macroinvertebrates,Deep learning,Convolutional Neural Networks

论文评审过程：Received 22 June 2017, Revised 24 April 2018, Accepted 21 June 2018, Available online 4 July 2018, Version of Record 5 September 2018.

论文官网地址：https://doi.org/10.1016/j.imavis.2018.06.005