On Confidences and Their Use in (Semi-)Automatic Multi-Image Taxa Identification
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 |
| Publisher | IEEE |
| Pages | 1338-1343 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728124858 |
| ISBN (Print) | 978-1-7281-2486-5 |
| DOIs | |
| Publication status | Published - 2019 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE Symposium Series on Computational Intelligence - Duration: 1 Jan 1900 → … |
Conference
| Conference | IEEE Symposium Series on Computational Intelligence |
|---|---|
| Abbreviated title | IEEE SSCI |
| Period | 1/01/00 → … |
Abstract
We analyzed classification confidences in biological multi-image taxa identification problems, where each specimen is represented by multiple images. We observed that confidences can be exploited to progress toward semi-automated identification process, where images are initially classified using a convolutional neural network and taxonomic experts manually inspect only the samples with a low confidence. We studied different ways to evaluate confidences and concluded that the difference of the largest and second largest values in unnormalized network outputs leads to best results. Furthermore, we compared different ways to use image-wise confidences when deciding on the final identification using all the input images of a specimen. The best results were obtained using a confidence-weighted sum rule over the unnormalized outputs. This approach also outperformed the evaluated supervised decision method.
ASJC Scopus subject areas
Keywords
- benthic macroinvertebrates, classification confidence, decision rules, taxa identification