论文标题
基于相似性的数据挖掘，用于在线域改编的声纳ATR系统
Similarity-based data mining for online domain adaptation of a sonar ATR system
论文作者
论文摘要
由于现场数据收集的昂贵性质，缺乏培训数据通常会限制自动目标识别（ATR）系统的性能。这个问题通常通过域的适应技术解决，但是当前现有的方法无法满足资源的限制和时间限制的水下系统。我们建议通过使用新颖的数据选择方法对ATR算法进行在线微调解决此问题。我们提出的数据挖掘方法依赖于视觉相似性，并胜过传统上采用的硬挖掘方法。我们在广泛的模拟环境中提出了比较性能分析，并突出了使用我们的方法快速适应以前看不见的环境的好处。
Due to the expensive nature of field data gathering, the lack of training data often limits the performance of Automatic Target Recognition (ATR) systems. This problem is often addressed with domain adaptation techniques, however the currently existing methods fail to satisfy the constraints of resource and time-limited underwater systems. We propose to address this issue via an online fine-tuning of the ATR algorithm using a novel data-selection method. Our proposed data-mining approach relies on visual similarity and outperforms the traditionally employed hard-mining methods. We present a comparative performance analysis in a wide range of simulated environments and highlight the benefits of using our method for the rapid adaptation to previously unseen environments.
