Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

doi:10.1007/s40436-018-0227-8

Advances in Manufacturing ›› 2018, Vol. 6 ›› Issue (4): 409-418.doi: 10.1007/s40436-018-0227-8

Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

Zhong-Hua Miao¹, Chen-Hui Ma¹, Zhi-Yuan Gao¹, Ming-Jun Wang², Cheng-Liang Liu²

1 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China;
2 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China

收稿日期:2017-06-21 修回日期:2018-06-07 出版日期:2018-12-25 发布日期:2018-12-08
通讯作者: Zhi-Yuan Gao,gaozhiyuan86@shu.edu.cn E-mail:gaozhiyuan86@shu.edu.cn
基金资助:
This paper was sponsored by the National Natural Science Foundation of China (Grant No. 51375293) and the Basic Research of the Science and Technology Commission of Shanghai Municipality (Grant No. 12JC1404100).

Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

Zhong-Hua Miao¹, Chen-Hui Ma¹, Zhi-Yuan Gao¹, Ming-Jun Wang², Cheng-Liang Liu²

1 School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China;
2 School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China

Received:2017-06-21 Revised:2018-06-07 Online:2018-12-25 Published:2018-12-08
Contact: Zhi-Yuan Gao,gaozhiyuan86@shu.edu.cn E-mail:gaozhiyuan86@shu.edu.cn

摘要/Abstract

摘要： An online hidden feature extraction algorithm is proposed for unknown and unstructured agricultural environments based on a supervised kernel locally linear embedding (SKLLE) algorithm. Firstly, an online obtaining method for scene training samples is given to obtain original feature data. Secondly, Bayesian estimation of the a posteriori probability of a cluster center is performed. Thirdly, nonlinear kernel mapping function construction is employed to map the original feature data to hyper-highdimensional kernel space. Fourthly, the automatic determination of hidden feature dimensions is performed using a local manifold learning algorithm. Then, a low-level manifold computation in hidden space is completed. Finally, long-range scene perception is realized using a 1-NN classifier. Experiments are conducted to show the effectiveness and the influence of parameter selection for the proposed algorithm. The kernel principal component analysis (KPCA), locally linear embedding (LLE), and supervised locally linear embedding (SLLE) methods are compared under the same experimental unstructured agricultural environment scene. Test results show that the proposed algorithm is more suitable for unstructured agricultural environments than other existing methods, and that the computational load is significantly reduced.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-018-0227-8

关键词: Long-range scene perception, Hidden feature extraction, Agricultural vehicle, Unstructured agricultural environment

Abstract: An online hidden feature extraction algorithm is proposed for unknown and unstructured agricultural environments based on a supervised kernel locally linear embedding (SKLLE) algorithm. Firstly, an online obtaining method for scene training samples is given to obtain original feature data. Secondly, Bayesian estimation of the a posteriori probability of a cluster center is performed. Thirdly, nonlinear kernel mapping function construction is employed to map the original feature data to hyper-highdimensional kernel space. Fourthly, the automatic determination of hidden feature dimensions is performed using a local manifold learning algorithm. Then, a low-level manifold computation in hidden space is completed. Finally, long-range scene perception is realized using a 1-NN classifier. Experiments are conducted to show the effectiveness and the influence of parameter selection for the proposed algorithm. The kernel principal component analysis (KPCA), locally linear embedding (LLE), and supervised locally linear embedding (SLLE) methods are compared under the same experimental unstructured agricultural environment scene. Test results show that the proposed algorithm is more suitable for unstructured agricultural environments than other existing methods, and that the computational load is significantly reduced.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-018-0227-8

Key words: Long-range scene perception, Hidden feature extraction, Agricultural vehicle, Unstructured agricultural environment

Zhong-Hua Miao, Chen-Hui Ma, Zhi-Yuan Gao, Ming-Jun Wang, Cheng-Liang Liu. Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling[J]. Advances in Manufacturing, 2018, 6(4): 409-418.

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Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

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