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Information 2019, 10(2), 37; https://doi.org/10.3390/info10020037

Object Recognition Using Non-Negative Matrix Factorization with Sparseness Constraint and Neural Network

1
School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China
2
School of Science, Xi’an Technological University, Xi’an 710021, China
*
Author to whom correspondence should be addressed.
Received: 11 November 2018 / Revised: 29 December 2018 / Accepted: 17 January 2019 / Published: 22 January 2019
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Abstract

UAVs (unmanned aerial vehicles) have been widely used in many fields, where they need to be detected and controlled. Small-sample UAV recognition requires an effective detecting and recognition method. When identifying a UAV target using the backward propagation (BP) neural network, fully connected neurons of BP neural network and the high-dimensional input features will generate too many weights for training, induce complex network structure, and poor recognition performance. In this paper, a novel recognition method based on non-negative matrix factorization (NMF) with sparseness constraint feature dimension reduction and BP neural network is proposed for the above difficulties. The Edgeboxes are used for candidate regions and Log-Gabor features are extracted in candidate target regions. In order to avoid the complexity of the matrix operation with the high-dimensional Log-Gabor features, preprocessing for feature reduction by downsampling is adopted, which makes the NMF fast and the feature discriminative. The classifier is trained by neural network with the feature of dimension reduction. The experimental results show that the method is better than the traditional methods of dimension reduction, such as PCA (principal component analysis), FLD (Fisher linear discrimination), LPP (locality preserving projection), and KLPP (kernel locality preserving projection), and can identify the UAV target quickly and accurately. View Full-Text
Keywords: non-negative matrix factor (NMF); sparseness constraint; back propagation (BP) neural network; feature dimension reduction non-negative matrix factor (NMF); sparseness constraint; back propagation (BP) neural network; feature dimension reduction
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Lei, S.; Zhang, B.; Wang, Y.; Dong, B.; Li, X.; Xiao, F. Object Recognition Using Non-Negative Matrix Factorization with Sparseness Constraint and Neural Network. Information 2019, 10, 37.

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