During the past decade breakthroughs in GPU hardware and deep neural networks technologies have revolutionized the field of computer vision, making image analytical potentials accessible to a range of real-world applications. Technology Assisted Review (TAR) in electronic discovery though traditionally has dominantly dealt with textual content, is witnessing a rising need to incorporate multimedia content in the scope. We have developed innovative image analytics applications for TAR in the past years, such as image classification, image clustering, and object detection, etc. In this paper, we discuss the use of image clustering applications to facilitate TAR based on our experiences in serving clients. We describe our general workflow on leveraging image clustering in tasks and use statistics from real projects to showcase the effectiveness of using image clustering in TAR. We also summarize lessons learned and best practices on using image clustering in TAR.

翻译：在过去十年中,GPU硬件和深层神经网络技术的突破使计算机视野领域发生了革命性的变化,使图像分析潜力能够为一系列现实世界应用所利用。电子发现方面的技术协助审查(TAR)虽然传统上主要处理文字内容,但发现电子发现方面的技术协助审查(TAR)日益需要将多媒体内容纳入范围。我们在过去几年中为TAR开发了创新性图像分析应用软件,如图像分类、图像组合和物体探测等。在本文中,我们根据客户服务的经验,讨论了利用图像组合应用程序促进TAR。我们描述了我们在任务中利用图像组合的通用工作流程,并利用实际项目的统计数据展示使用TAR图像组合的有效性。我们还总结了在TAR中使用图像组合的经验教训和最佳做法。