Benchmark database for fine-grained image classification of benthic macroinvertebrates
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||11|
|Journal||Image and Vision Computing|
|Publication status||Published - 1 Oct 2018|
|Publication type||A1 Journal article-refereed|
Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.
- Benthic macroinvertebrates, Biomonitoring, Convolutional Neural Networks, Deep learning, Fine-grained classification