Recognition algorithm for plant leaves based on adaptive supervised locally linear embedding
Keywords:
supervised locally linear embedding, manifold learning, Fisher projection, adaptive neighbors, leaf recognition, Precision AgricultureAbstract
Locally linear embedding (LLE) algorithm has a distinct deficiency in practical application. It requires users to select the neighborhood parameter, k, which denotes the number of nearest neighbors. A new adaptive method is presented based on supervised LLE in this article. A similarity measure is formed by utilizing the Fisher projection distance, and then it is used as a threshold to select k. Different samples will produce different k adaptively according to the density of the data distribution. The method is applied to classify plant leaves. The experimental results show that the average classification rate of this new method is up to 92.4%, which is much better than the results from the traditional LLE and supervised LLE.References
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[2] Gu X, Du J X. Leaf recognition based on the skeleton segmentation. Lecture Notes in Computer Science, 2005; 3644: 253-262.
[3] Du J X. Study of plant leaf recognition techniques by machine. University of Science and Technology of China, PhD thesis, 2005, China. (In Chinese with English abstract)
[4] Li Y F, Zhu Q S, Cao Y K, Wang C L. A leaf vein extraction method based on snakes technique. Proceedings of IEEE International Conference on Neural Networks and Brain, 2005: 885-888.
[5] Neto J C, Meyer G E, Jones D D, Samal A K. Plant species identification using Elliptic Fourier leaf shape analysis. Computers and Electronics in Agriculture, 2006; 50(2): 121- 134.
[6] Bruno O M, Plotze R O, Falvo M, de Castro M. Fractal dimension applied to plant identification. Information Science, 2008; 178(12): 2722-2733.
[7] Wang X F, Huang D S, Du J X, Zhang G J. Feature extraction and recognition for leaf images. Computer Engineering and Applications, 2006; 2006(3): 190-193.
[8] Chen F B, Yang J Y. Modular PCA and its application in human face recognition. Computer Engineering and Design, 2007; 28(8): 1889-1892.
[9] Wang H M, Ou Z Y. Face recognition based on features by PCA/ICA and classification with SVM. Journal of Computer Aided Design & Computer Graphics, 2003; 15(4): 416-420. (In Chinese with English abstract)
[10] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000; 290(5500): 2323-2326.
[11] Li B, Yang D, Lei M, Ge Y X. Adaptive locally linear embedding based on affinity propagation. Journal of Optoelectronics Laser, 2010; 21(5): 772-778.
[12] Wen G H, Jiang L J, Wen J. Locally linear embedding based on optimization of neighborhood. Journal of System Simulation, 2007; 19(13): 3119-3122. (In Chinese with English abstract)
[13] Wen G H, Jiang L J, Wen J. Dynamically determining neighborhood parameter for locally linear embedding.
Journal of Software, 2008; 19(7): 1666-1673. (In Chinese with English abstract)
[14] Yu J, Qin R X, Deng N Y. Locally linear embedding algorithm based on adaptive nearest neighbor. Control Engineering of China, 2006; 13(5): 469-470. (In Chinese with English abstract)
[15] Hui K H, Xiao B H, Wang C H. Self-regulation of neighborhood parameter for locally linear embedding. Pattern Recognition and Artificial Intelligence, 2010; 23(6): 842-846. (In Chinese with English abstract)
[16] Zhang Y L, Zhuang J, Wang N, Wang S A. Fusion of adaptive local linear embedding and spectral clustering algorithm with application to fault diagnosis. Journal of Xian Jiaotong University, 2010; 44(1): 77-82. (In Chinese with English abstract)
[17] Zhang X F, Huang S B. Adaptive neighborhoods based locally linear embedding algorithm. Journal of Harbin Engineering University, 2012; 33(4): 489-495. (In Chinese with English abstract)
[18] Zhang S W, Huang D S. A robust supervised manifold learning algorithm and its application to plant leaf classification. Pattern Recognition and Artificial Intelligence, 2010; 23(6):836-841. (In Chinese with English abstract)
[19] Yan Q, Liang D, Zhang J J. Recognition method of plant leaves based on Fisher projection-supervised LLE algorithm. Transactions of the Chinese Society for Agricultural Machinery, 2012; 43(9): 179-183. (In Chinese with English abstract)
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Published
2013-09-22
How to Cite
Qing, Y., Dong, L., Dongyan, Z., & Xiu, W. (2013). Recognition algorithm for plant leaves based on adaptive supervised locally linear embedding. International Journal of Agricultural and Biological Engineering, 6(3), 52–57. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/794
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Information Technology, Sensors and Control Systems
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