基于稀疏编码金字塔模型的农田害虫图像识别  

Recognition for insects via spatial pyramid model using sparse coding

作　　者：谢成军[1] 李瑞[1] 董伟[2] 宋良图[1] 张洁[1] 陈红波[1] 陈天娇[1] 

机构地区：[1]中国科学院合肥智能机械研究所,合肥230031 [2]安徽省农业科学院农业经济与信息研究所,合肥230031

出　　处：《农业工程学报》2016年第17期144-151,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然基金项目:基于上下文感知与稀疏表示的害虫图像识别研究(31401293);安徽省农业科学院院长青年创新基金项目:基于机器视觉的植保图像采集与元数据管理技术研究(14B1461)

摘　　要：相较于一般物体的图像,农作物害虫图像因具有复杂的农田环境背景,分类与识别更加困难。为提高害虫图像识别的准确率,该文提出一种基于图像稀疏编码与空间金字塔模型相结合的害虫图像表示与识别方法。该方法利用大量非标注的自然图像块构造过完备学习字典,并运用该学习字典实现对害虫图像的多空间稀疏表示。与此同时,结合多核学习,该文设计了一种害虫图像识别算法。通过对35种害虫的识别,试验结果表明:在相同方法下,该文所提特征提取方法可使平均识别精度提高9.5百分点;此外,进一步通过对221种昆虫及20种蝴蝶的识别,试验结果表明:与传统方法相比较,该文所提方法使得平均识别精度提高14.1百分点。Automatic classification of insect species in field crops such as corn, soybean, wheat, and canola is more difficult than the generic object classification because of complex background in filed and high appearance similarity among insect species. In this paper, we propose an insect recognition system on the basis of advanced sparse coding and spatial pyramid model. We firstly learn features from a large amount of unlabeled insect image patches to construct an over-complete dictionary. The sparse coding of insect image patches is obtained by encoding over the dictionary. To enhance discriminative ability of the sparse coding, we then apply multiple scales of filters coupled with different spaces. Finally, the multiple space features of sparse coding are seamlessly embedded into a multi-kernel framework for robust classification. Traditionally, insect recognition has mainly relied on manual identification by expert entomologists. However, for laymen without a thorough understanding of the terminology of insect taxonomy and morphological characteristics, it is hard to discriminate insect categories at the species level. Therefore, effective identification of insects is a key issue that needs to be well addressed. To improve the recognition accuracy, we develop an insect recognition system using advanced sparse coding, spatial pyramid model and multiple-kernel learning techniques. Different from traditional feature representation, a novel feature representation that is multiple-space sparse coding of insect objects is proposed by this work. The work flow of our method can be decomposed into 2 stages. The first stage focuses on image or insect object representation. At this stage, the features of insect images are extracted using advanced sparse coding and spatial pyramid model. The second stage, which deals with effective fusion of multiple insect-categorization features, constructs a kernel-level fusion classifier using all the sparse coding features. At the first stage, for an insect image given, the features of i

关 键 词：图像识别 算法 害虫控制 字典学习 稀疏编码 金字塔模型 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]