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Article

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

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School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China
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College of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Department of Applied Informatics, Vytautas Magnus University, Kaunas 44404, Lithuania
*
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(3), 434; https://doi.org/10.3390/sym12030434
Received: 3 February 2020 / Revised: 1 March 2020 / Accepted: 5 March 2020 / Published: 8 March 2020
Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets. View Full-Text
Keywords: manifold learning; feature mapping; infrared image recognition; object classification; Kullback-Leibler Divergence manifold learning; feature mapping; infrared image recognition; object classification; Kullback-Leibler Divergence
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MDPI and ACS Style

Ge, H.; Zhu, Z.; Lou, K.; Wei, W.; Liu, R.; Damaševičius, R.; Woźniak, M. Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points. Symmetry 2020, 12, 434. https://doi.org/10.3390/sym12030434

AMA Style

Ge H, Zhu Z, Lou K, Wei W, Liu R, Damaševičius R, Woźniak M. Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points. Symmetry. 2020; 12(3):434. https://doi.org/10.3390/sym12030434

Chicago/Turabian Style

Ge, Huilin, Zhiyu Zhu, Kang Lou, Wei Wei, Runbang Liu, Robertas Damaševičius, and Marcin Woźniak. 2020. "Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points" Symmetry 12, no. 3: 434. https://doi.org/10.3390/sym12030434

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