农林检疫害虫图像自动识别研究

项目名称： 农林检疫害虫图像自动识别研究

项目编号： No.61273289

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 侯新文

作者单位： 中国科学院自动化研究所

项目金额： 80万元

中文摘要： 随着全球气候异常和农产品进出口频繁，我国农林病虫害和外来物种入侵造成的损失逐年加重。利用计算机自动识别农林检疫害虫，建立大规模监测体系，事关我国粮食安全和生态安全。本项目面向自然场景中的农林检疫害虫图像，以解决害虫识别中的小训练样本、大类别集、姿态和视角变化的鲁棒特征提取等问题为目标，以计算机视觉、机器学习方法与昆虫图像特点相结合为手段，力求在这几个方面突破关键技术，为自然场景中的农林检疫害虫图像识别应用提供技术储备。针对小训练样本问题，我们将采用迁移学习方法增加训练样本。针对大类别集问题，我们将采用非线性距离度量、图像检索方法设计出适用于百类乃至千类的图像分类方法。在鲁棒特征方面，我们结合昆虫形态特点，利用显著性特征、多特征融合和图匹配等方法设计出姿态和视角不变的鲁棒特征。本项目也涉及大量昆虫标本获取和图像采集，将建立一个包含3个目17个属100个种的2000个昆虫标本以及相关的图像库。

中文关键词： 昆虫识别；特征生成；稀疏编码；昆虫形态特征；异质类别集融合

英文摘要： With the global climate becoming more abnormal and agriculture product imports and exports increasing, the loss by harmful insects and foreign species intrusion is more and more severe. So it is necessary for our food and zoology safety to eatabilish large scale inspecting sysytem by automated recognizing harmful farm and quarantine insects by computer. This project is faced on harmful farm and quarantine insect images captured under free pose in natural scene, aims at the small training set, large category set, pose and view point robust feature extraction problems. We will combining computer vision, machine learning and insect appearance characteristic, break through several key technologies and provide technique deposite for large scale application of harmful farm and quarantine insects. Regarding the small training set problem, we will increasing the training samples by transfer learning. Regarding the large large category set problem ,we will design classification techniques for handreds upto thousand categories by nonlinear metric learning and image retrival. Regarding robust feature extraction problem, we start from the shape and appearance characteristic of insects, design pose and view point invariant features by sailent feature extraction, multiple feature fusion and graph matching. We will also provie

英文关键词： insect recognition；feature generation；parse coding；insect morphology feature；heterogeneous class sets fusing