| 基于内容的小麦害虫图像检索系统研究与实现 |
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| 引用本文： | 李峥嵘,刘月娥,何东健,龙满生,刘全中.基于内容的小麦害虫图像检索系统研究与实现[J].农业工程学报,2007,23(11):210-215. |
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| 作者姓名： | 李峥嵘 刘月娥 何东健 龙满生 刘全中 |
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| 作者单位： | 1. 西北农林科技大学信息工程学院,杨凌,712100
2. 澳大利亚昆士兰科技大学信息技术系,澳大利亚布里斯班,4001 |
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| 基金项目： | 西北农林科技大学重点科研专项基金 |
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| 摘 要： | 以小麦害虫图像为研究对象,研究并开发了基于内容的害虫图像检索系统。重点研究了基于内容的图像检索中的图像特征提取、图像相似性度量和用户相关反馈技术。提出一种重叠四分块颜色矩和一种基于BP神经网络的图像相似性度量方法,并引入灰色相关反馈算法实现了基于语义的图像检索。应用排序评价方法、查准率与查全率对系统的检索性能进行测试,结果表明,系统具有一定的实用性,为快速准确地诊断、识别农作物害虫和害虫图像资源共享提供了技术依据。
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| 关 键 词： | 小麦害虫图像 基于内容的图像检索 BP神经网络 灰色相关反馈 |
| 文章编号： | 1002-6819（2007）11-0210-06 |
| 收稿时间： | 2007-02-03 |
| 修稿时间： | 2007-08-22 |
Investigation and implementation of content-based retrieval system for wheat pest images |
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| Li Zhengrong,Liu Yue''e,He Dongjian,Long Mansheng and Liu Quanzhong.Investigation and implementation of content-based retrieval system for wheat pest images[J].Transactions of the Chinese Society of Agricultural Engineering,2007,23(11):210-215. |
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| Authors: | Li Zhengrong Liu Yue'e He Dongjian Long Mansheng and Liu Quanzhong |
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| Affiliation: | College of Information Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China;Faculty of Information Technology, Queensland University of Technology, Brisbane 4001, Australia;College of Information Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China;College of Information Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China;College of Information Engineering, Northwest Agriculture and Forestry University, Yangling 712100, China |
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| Abstract: | Taking wheat pest images as a case study, techniques in a content-based image retrieval system were studied including image feature extraction, image similarity measurement and user relevant feedback technologies. Color moments based on four overlapping division and a similarity measurement method based on BP neural network were prompted for the improvement of image retrieval. Besides, the grey relevant feedback was improved to accomplish semantic based image retrieval. A content-based pest retrieval system was developed. And the evaluation results on this system through the sorting evaluation method, precision rate and recall rate show that it is practical to a certain extent, which provides technological support for quick crop pest diagnosis and recognition, and the sharing of crop pest images. |
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| Keywords: | wheat pest image content-based image retrieval system BP neural network grey relevant feedback |
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