基于叶片图像和监督正交最大差异伸展的植物识别方法

doi:10.11707/j.1001-7488.20130627

林业科学 ›› 2013, Vol. 49 ›› Issue (6): 184-188.doi: 10.11707/j.1001-7488.20130627

基于叶片图像和监督正交最大差异伸展的植物识别方法

张善文^1,2, 张传雷², 王旭启¹, 周争光¹, 张雅丽³

1. 西京学院工程技术系西安 710123;
2. Ryerson大学电子与计算机工程系 M5B 2K;
3. 西北农林科技大学杨凌 712100

收稿日期:2012-07-30 修回日期:2012-11-18 出版日期:2013-06-25 发布日期:2013-07-16
通讯作者: 张传雷
基金资助:
国家自然科学基金项目(61272333)。

Plant Recognition Based on Leaf Image and Supervised Orthogonal Maximum Variance Unfolding

Zhang Shanwen^1,2, Zhang Chuanlei², Wang Xuqi¹, Zhou Zhengguang¹, Zhang Yali³

1. Department of Engineering and Technology,Xijing University Xi'an 710123;
2. Department of Electrical and Computer Engineering, Ryerson University M5B 2K3. , Canada;
3. Northwest A & F University Yangling 712100

Received:2012-07-30 Revised:2012-11-18 Online:2013-06-25 Published:2013-07-16

摘要/Abstract

关键词: 流形学习, 植物叶片识别, 最大差异伸展, 监督正交最大差异伸展

Abstract:

Due to the large difference between the same-class leaf images, many classical recognition methods do not satisfy the actual requirements of the plant leaf image recognition system. Based on maximum variance unfolding(MVU)and maximum variance projection(MVP), a supervised orthogonal MVU algorithm was presented and was applied to plant leaf image recognition. By the algorithm, the high-dimensionality data were mapped to an optimal low-dimensionality subspace where the different-class samples were located further away, while the same-class samples were located closer. The local geometry structure of the low dimension manifold of the original high dimensionality data was preserved. The experimental results on real plant leaf databases showed that the proposed method was effective and feasible for plant leaf recognition.

Key words: manifold learning, plant leaf recognition, maximum variance unfolding(MVU), supervised orthogonal MVU

中图分类号:

TP391

张善文, 张传雷, 王旭启, 周争光, 张雅丽. 基于叶片图像和监督正交最大差异伸展的植物识别方法[J]. 林业科学, 2013, 49(6): 184-188.

Zhang Shanwen, Zhang Chuanlei, Wang Xuqi, Zhou Zhengguang, Zhang Yali. Plant Recognition Based on Leaf Image and Supervised Orthogonal Maximum Variance Unfolding[J]. Scientia Silvae Sinicae, 2013, 49(6): 184-188.

参考文献

杜吉祥. 2005. 植物物种机器识别技术的研究. 合肥: 中国科学技术大学博士学位论文.

纪寿文,王荣本,陈佳娟, 等. 2002. 应用计算机图像处理技术识别玉米苗期田间杂草的研究.农业工程学报, 18(4): 150-154.

李波. 2008. 基于流形学习的特征提取方法及其应用研究.合肥: 中国科学技术大学博士学位论文.

王晓峰,黄德双,杜吉祥,等. 2006. 叶片图像特征提取与识别技术的研究.计算机工程与应用, 42(3): 190-193.

徐贵力,毛罕平,李萍萍. 2002. 缺素叶片彩色图像颜色特征提取的研究.农业工程学报,4(3): 150-153.

张宁,刘文萍. 2011. 基于图像分析的植物叶片识别技术综述.计算机应用研究,28(11): 68-71.

张善文,巨春芬. 2010. 正交全局-局部判别映射应用于植物叶片分类.农业工程学报, 26(10): 162-165.

Arun Priya C, Balasaravanan T. 2012. An efficient leaf recognition algorithm for plant classification using support vector machine. Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, 428-432.

Du J X, Zhai C M.2010. Plant species recognition based on radial basis probabilistic neural networks ensemble classifier. Lecture Notes in Artificial Intelligence, 6216: 677-681.

He X, Yan S, Hu Y,et al. 2005. Face recognition using laplacianfaces. IEEE Transations on Pattern Analysis and Machine Intellegence, 27(3): 328-340.

Liu J M.2012. A new plant leaf classification method based on neighborhood rough set. Advances in information Sciences and Service Sciences(AISS), 4(1): 116-124.

Mokhtarian F, Abbasi S.2004. Matching shapes with self-intersection: application to leaf classification. IEEE Transactions on Image Processing, 13(5): 653-661.

Roweis S T, Saul L K.2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500): 2323-2326.

Söderkvist O.2001. Computer vision classification of leaves from Swedish trees. Master's Thesis, Linkoping University.

Tenenbaum J B, De Silva V, Langford J C.2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500): 2319-2323.

Wang X F, Huang D S, Du J X,et al. 2008. Classification of plant leaf images with complicated background. Applied Mathematics and Computation, 205(2): 916-926.

Wang Z, Chi Z, Feng D, et al. 2000. Leaf image retrieval with shape features. Lecture Notes in Computer Science, 1929: 477-487.

Wang Z, Chi Z, Feng D.2003. Shape based Leaf Image Retrieval. IEEE Transactions on Image Signal Process, 150(1): 34-43.

Weinberger K Q, Saul L K.2006. An introduction to nonlinear dimensionality reduction by maximum variance unfolding. American Association for Artificial Intelligence, 1683-1686.

Zhang T, Yang J, Wang H,et al. 2007. Maximum variance projections for face recognition. Optical Engineering, 46(6): 1-4.

[1]	宋以宁, 刘文萍, 骆有庆, 宗世祥. 基于线性谱聚类的林地图像中枯死树监测[J]. 林业科学, 2019, 55(4): 187-195.
[2]	郭慧, 王霄, 刘传泽, 周玉成. 基于灰度共生矩阵和分层聚类的刨花板表面图像缺陷提取方法[J]. 林业科学, 2018, 54(11): 111-120.
[3]	刘传泽, 王霄, 陈龙现, 郭慧, 罗瑞, 周玉成. 基于随机森林算法的纤维板表面缺陷识别[J]. 林业科学, 2018, 54(11): 121-126.
[4]	陈龙现, 葛浙东, 罗瑞, 刘传泽, 刘晓平, 周玉成. 基于CNN的木材内部CT图像缺陷辨识[J]. 林业科学, 2018, 54(11): 127-133.
[5]	郭慧, 王霄, 刘传泽, 周玉成. 人造板表面缺陷检测图像自适应快速阈值分割算法[J]. 林业科学, 2018, 54(11): 134-142.
[6]	李永亮, 张怀清, 杨廷栋, 马载阳, 贺长平, 李思佳. 模糊逻辑与复合顺序形态学相结合的叶脉检测方法[J]. 林业科学, 2018, 54(5): 70-77.
[7]	戚玉涵, 徐佳鹤, 张星梅, 葛浙东, 李早芳, 周玉成. 基于扇形X射线束的立木CT成像系统[J]. 林业科学, 2016, 52(7): 121-128.
[8]	张国梁, 蔡小娜, 侯晓鹏, 赵旦, 周玉成, 葛浙东. 面向零件的人造板材矩形件锯切排样数学建模及遗传算法求解[J]. 林业科学, 2016, 52(5): 150-159.
[9]	任洪娥, 沈雯雯, 白杰云, 官俊. 基于最小二乘法的落叶松木粉粒径数学建模与分析[J]. 林业科学, 2015, 51(4): 164-170.
[10]	张国梁, 侯晓鹏, 苗虎, 安源, 周玉成, 姚永和. 基于分组降维规则和遗传算法的人造板材矩形件优化下料方法[J]. 林业科学, 2014, 50(6): 181-186.
[11]	刘子豪, 祁亨年, 张广群, 汪杭军. 基于横切面微观构造图像的木材识别方法[J]. 林业科学, 2013, 49(11): 116-121.
[12]	刘子豪, 汪杭军. 基于PCA+FisherTrees特征融合的木材识别[J]. 林业科学, 2013, 49(6): 122-128.
[13]	周新年;胡喜生;陈辉荣;郑丽凤;林海明;巫志龙;周成军;郑端生. 天然次生林不同择伐强度后林分生长动态仿真[J]. , 2013, 49(1): 134-141.
[14]	谢小华;唐丽玉;邹杰;陈崇成. 基于本体规则和案例推理相结合的树木生长模拟——以杉木为例[J]. 林业科学, 2012, 48(9): 68-75.
[15]	汪杭军;汪碧辉. 一种新的针叶材自动识别方法[J]. 林业科学, 2011, 47(10): 141-145.

基于叶片图像和监督正交最大差异伸展的植物识别方法

Plant Recognition Based on Leaf Image and Supervised Orthogonal Maximum Variance Unfolding

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

作者中心

期刊获奖

引证指标

联系方式