The Herbarium Challenge 2019 Dataset
By Kiat Chuan Tan, Yulong Liu, Barbara A. Ambrose, Melissa C. Tulig, Serge J. Belongie. 2019
89 citations recorded by the source corpus.
Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.
Subfields: Cell Biology, Computer Vision and Pattern Recognition, Ecological Modeling, Plant Science, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Kai Han
- Andrea Vedaldi
- Andrew Zisserman
- Luc Van Gool
- Yi Yang
- Ming-Hsuan Yang
- Trevor Darrell
- Jitendra Malik
In Plant Science
- Andrew Zisserman
- Tinne Tuytelaars
- Rahaf Aljundi
- Maria-Elena Nilsback
- Arun Mallya
- Serge J. Belongie
- Svetlana Lazebnik
- Trevor Darrell
In Evaluation & Benchmarks
- Andrea Vedaldi
- Andrew Zisserman
- Kai Han
- Sagar Vaze
- Cordelia Schmid
- Florent Perronnin
- Kaiming He
- Jitendra Malik
Citations in the corpus
- Generalized Category Discovery (2022, methodology): “…Herbarium19 dataset itself represents a real-world use case for GCD: while we are aware of roughly 400k species of plants, and estimate that there are around 80k yet to be discovered, it currently takes roughly 35 years from plant collection to plant species description if performed manually [42].”