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Infrared vehicle recognition using unsupervised feature learning based on K-Feature

机译：基于K特征的无监督特征学习的红外车辆识别

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摘要

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 K-means 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.
机译：在复杂的战场环境下，难以在车辆识别算法的实际应用中建立完整的知识库。红外车辆识别始终是困难和挑战性的，这在遥感中起着重要的作用。在本文中，我们提出了一种新的基于K特征的无监督特征学习方法来识别红外图像中的车辆。首先，我们使用基于显着性的目标检测算法来检测初始图像。然后，通过基于K-means聚类算法生成的基于K特征的无监督特征学习来计算误报并提高准确性，该K-means聚类算法是通过从大量没有标签的样本中学习视觉词典来提取特征而提取的。最后，通过一些后处理完成车辆目标识别图像。大量实验表明，该方法在复杂背景下的红外图像中具有令人满意的识别能力和鲁棒性，并提高了其可靠性。

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