基于稀疏表示的多特征融合害虫图像识别<sup>*</sup>

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摘要为提高害虫图像识别的准确率，针对不同害虫具有不同的颜色、形状、纹理的特点，提出一种将颜色、形状、纹理特征与稀疏表示相融合的害虫识别方法.该方法利用已标注的训练样本构造不同特征下的训练样本矩阵，通过求解样本的最优稀疏系数以实现害虫图像识别.由于相同样本通过不同特征训练字典求解的稀疏系数不同，进而识别结果也不同.因此，文中进一步通过设计不同特征下的识别分类器实现多特征的融合.在实验室环境与农田环境下的实验结果表明，相较于其他方法，该方法的害虫识别率获得较大的提高.

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	胡永强
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关键词 ：害虫识别, 特征提取, 稀疏表示, 多特征融合, AdaBoost

Abstract：Aiming at the characteristics of different pest images with different colors, shapes and textures, a pest recognition method based on sparse representation and multi-feature fusion is proposed, which uses a matrix of labeled training samples to construct different dictionaries. The recognition result is achieved by solving optimal sparse coefficients with the corresponding feature dictionary. Furthermore, a novel learning method, which can be improved efficiently by jointly optimizing classifier weights, is presented to effectively fuse multiple features for pest categorization. The experimental results on real datasets show that the proposed method performs well on pest species recognition either in laboratory or in farmland.

Key words： Pest Recognition Feature Extraction Sparse Representation Multi-feature Fusion AdaBoost

收稿日期: 2014-08-19

ZTFLH:

TP391.4

基金资助:国家科技支撑计划项目(No.2012BAK17B15)资助

作者简介: 胡永强，男，1970年生，副研究员，主要研究方向为智能决策和智能信息系统.E-mail:yqhuu@163.com.宋良图(通讯作者)，男，1963年生，博士，研究员，主要研究方向为智能信息处理与控制系统.E-mail:ltsong@iim.ac.cn.张洁，男，1982年生，博士研究生，主要研究方向为图像处理、模式识别.谢成军，男，1979年生，博士研究生，主要研究方向为计算机视觉、视频处理.李瑞，男，1985年生，硕士研究生，主要研究方向为模式识别、图像处理.

引用本文:

胡永强，宋良图，张洁，谢成军，李瑞. 基于稀疏表示的多特征融合害虫图像识别^*[J]. 模式识别与人工智能, 2014, 27(11): 985-992. HU Yong-Qiang, SONG Liang-Tu, ZHANG Jie, XIE Cheng-Jun, LI Rui. Pest Image Recognition of Multi-feature Fusion Based on Sparse Representation. , 2014, 27(11): 985-992.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2014/V27/I11/985

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