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


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Article Info

DATE

2018-12

AUTHORS

Zhong-Hua Miao, Chen-Hui Ma, Zhi-Yuan Gao, Ming-Jun Wang, Cheng-Liang Liu

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-high-dimensional 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. More... »

PAGES

1-10

References to SciGraph publications

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  • 2017-02. An automated 3D modeling of topological indoor navigation network in GEOJOURNAL
  • 2014-03. Computer vision supported by 3D geometric modelling in ADVANCES IN MANUFACTURING
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  • 1997. Kernel principal component analysis in ARTIFICIAL NEURAL NETWORKS — ICANN'97
  • 2003. Supervised Locally Linear Embedding in ARTIFICIAL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING — ICANN/ICONIP 2003
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40436-018-0227-8

    DOI

    http://dx.doi.org/10.1007/s40436-018-0227-8

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    https://app.dimensions.ai/details/publication/pub.1105438723


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