论文标题
通过一级分类来增强稀有底栖大型无脊椎动物分类群识别
Boosting rare benthic macroinvertebrates taxa identification with one-class classification
论文作者
论文摘要
昆虫监测对于理解快速生态变化的后果至关重要，但是当前需要进行乏味的手动专家工作，并且不能有效地扩大规模。深卷积神经网络（CNN），提供了一种可行的方法来显着增加生物监测量。但是，分类群的丰度通常非常不平衡，对于深CNN而言，最稀有类的训练图像太低了。结果，稀有类别的样本通常完全被完全遗漏，而检测它们具有生物学重要性。在本文中，我们提出将训练有素的深CNN与一级分类器相结合，以改善稀有物种的识别。传统上，对单级分类模型进行了较少的样本训练，它们可以提供一种机制，以指示可能属于罕见类的样品进行人类检查。我们的实验证实，所提出的方法确实可以支持朝着部分自动化分类识别任务迈进。
Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task.
