论文标题
昆虫图像数据中生物因素的机器学习挑战
Machine Learning Challenges of Biological Factors in Insect Image Data
论文作者
论文摘要
Bioscan项目由国际生命财团的国际条形码领导，试图在全球范围内研究生物多样性的变化。该项目的一个组成部分是研究所有昆虫的物种相互作用和动态。除了基因条形码昆虫外，每年还会收集超过150万张图像，每个图像都需要分类分类。随着传入图像的大量图像，仅依靠专家分类学家来标记这些图像是不可能的；但是，人工智能和计算机视觉技术可能会提供可行的高通量解决方案。其他任务，包括手动称重个体昆虫来确定生物量，保持乏味和昂贵。再次在这里，计算机视觉可能提供有效且引人注目的替代方案。虽然使用计算机视觉方法吸引了解决这些问题，但生物学因素出现的重大挑战。这些挑战是在本文中的机器学习的背景下提出的。
The BIOSCAN project, led by the International Barcode of Life Consortium, seeks to study changes in biodiversity on a global scale. One component of the project is focused on studying the species interaction and dynamics of all insects. In addition to genetically barcoding insects, over 1.5 million images per year will be collected, each needing taxonomic classification. With the immense volume of incoming images, relying solely on expert taxonomists to label the images would be impossible; however, artificial intelligence and computer vision technology may offer a viable high-throughput solution. Additional tasks including manually weighing individual insects to determine biomass, remain tedious and costly. Here again, computer vision may offer an efficient and compelling alternative. While the use of computer vision methods is appealing for addressing these problems, significant challenges resulting from biological factors present themselves. These challenges are formulated in the context of machine learning in this paper.
