RI: Machine Learning for Robust Recognition of Invertebrate Specimens in Ecological Science

RI：机器学习在生态科学中对无脊椎动物标本的鲁棒识别

• 批准号: 0705765
• 负责人: Thomas Dietterich
• 金额: $ 80万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705765&HistoricalAwards=false
• 关键词: RI; Machine; Learning; Robust; Recognition

Proposal 0705765PIs: Thomas Dietterich, David Lytle, Andrew Moldenke, Robert Paasch, Eric Mortensen, Linda ShapiroInstitution: Oregon State UniversityTitle: RI: Machine Learning for Robust Recognition of Invertebrate Specimens in Ecological ScienceAbstractAn interdisciplinary team of computer scientists, mechanical engineers, and entomologists from Oregon State University and the University of Washington are developing computer vision, machine learning, and robotic methods for high-precision generic object recognition and applying these methods to the imaging and classification of invertebrate specimens of soil mesofauna and freshwater zooplankton. Current manual methods for recognizing and counting these organisms are extremely tedious and time-consuming, and require a high degree of expertise. Automated, rapid-throughput population counting will provide a revolutionary new tool for ecologists to understand and monitor soil and freshwater ecosystems. Soil arthropods form a central component of ecological processes in soils, so accurate soil arthopod population counting is critical to improving our understanding of ecosystem functions and community ecology. Freshwater zooplankton species are a fundamental component of many ecosystems, because they transfer energy from primary producers to consumers such as fish and birds. Zooplankton also serve as a model system for understanding basic ecosystem processes, predator-prey dynamics, and disease ecology.Automated recognition of these organisms poses difficult classification problems because it requires much more precise discrimination than generic object recognition tasks of the type commonly studied in computer vision. Current approaches to generic object recognition employ a bag-of-keypoints methodology in which hand-crafted region detectors, hand-crafted region descriptors, and unsupervised feature dictionaries are applied to convert an image into a fixed-length feature vector. Machine learning is only employed at the final step to classify this feature vector into a generic object class. This project seeks to integrate machine learning into all aspects of the vision pipeline. It will develop and test discriminative learning algorithms for the automated discovery of region detectors, region descriptors, feature dictionaries, and classifiers. To reduce the risk of overfitting, sub-part correspondences and spatial constraints will be imposed to constrain the learning algorithms. In addition to discriminative methods, the investigators will also learn generative models to help reject debris and unknown species that appear in the images. Model adaptation methods will be developed to take advantage of the fact that in any given biological sample, organisms of the same taxon tend to be more similar to each other than they are when samples from multiple sites are pooled.Progress on this project will be regularly reported at http:// web.engr.oregonstate.edu/~tgd/bugid/

提案0705765个人：Thomas Dietterich， David Lytle, Andrew Moldenke, Robert Paasch, Eric Mortensen， Linda shapiro机构：俄勒冈州立大学标题：RI一个由来自俄勒冈州立大学和华盛顿大学的计算机科学家、机械工程师和昆虫学家组成的跨学科团队正在开发用于高精度通用目标识别的计算机视觉、机器学习和机器人方法，并将这些方法应用于土壤中系动物和淡水浮游动物的无脊椎动物标本的成像和分类。目前用于识别和计数这些生物的人工方法非常繁琐和耗时，并且需要高度的专业知识。自动化、快速的人口计数将为生态学家了解和监测土壤和淡水生态系统提供一种革命性的新工具。土壤节肢动物是土壤生态过程的核心组成部分，因此准确的土壤节肢动物种群计数对提高我们对生态系统功能和群落生态学的认识至关重要。淡水浮游动物是许多生态系统的基本组成部分，因为它们将能量从初级生产者转移到鱼类和鸟类等消费者。浮游动物也是理解基本生态系统过程、捕食者-猎物动力学和疾病生态学的模型系统。这些生物的自动识别带来了困难的分类问题，因为它需要比计算机视觉中通常研究的一般对象识别任务更精确的识别。当前的通用目标识别方法采用了一种关键点袋方法，其中使用手工制作的区域检测器、手工制作的区域描述符和无监督特征字典将图像转换为固定长度的特征向量。机器学习仅在最后一步使用，以将该特征向量分类为通用对象类。该项目旨在将机器学习整合到视觉管道的各个方面。它将开发和测试判别学习算法，用于自动发现区域检测器、区域描述符、特征字典和分类器。为了减少过度拟合的风险，我们将使用子部分对应和空间约束来约束学习算法。除了判别方法，研究人员还将学习生成模型，以帮助排除图像中出现的碎片和未知物种。模型适应方法将被开发，以利用这样一个事实，即在任何给定的生物样本中，同一分类单元的生物往往比来自多个地点的样本更相似。将定期在http:// web.engr.oregonstate.edu/~tgd/bugid/报告该项目的进展情况