Translator Disclaimer
19 February 2018 Infrared vehicle recognition using unsupervised feature learning based on K-feature
Author Affiliations +
Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 106080N (2018)
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Lin, Yihua Tan, Haijiao Xia, and Jinwen Tian "Infrared vehicle recognition using unsupervised feature learning based on K-feature", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080N (19 February 2018);


Back to Top