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Proceedings Paper

Infrared vehicle recognition using unsupervised feature learning based on K-feature
Author(s): Jin Lin; Yihua Tan; Haijiao Xia; Jinwen Tian
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Paper Abstract

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Paper Details

Date Published: 19 February 2018
PDF: 8 pages
Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080N (19 February 2018); doi: 10.1117/12.2288698
Show Author Affiliations
Jin Lin, Huazhong Univ. of Science and Technology (China)
Yihua Tan, Huazhong Univ. of Science and Technology (China)
Haijiao Xia, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10608:
MIPPR 2017: Automatic Target Recognition and Navigation
Jianguo Liu; Jayaram K. Udupa; Hanyu Hong, Editor(s)

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