Use Image Clustering to Facilitate Technology Assisted Review

TL;DR

This paper explores how image clustering techniques can enhance Technology Assisted Review (TAR) in electronic discovery by improving the analysis of multimedia content, supported by real project data and practical insights.

Contribution

The paper introduces the application of image clustering to TAR, demonstrating its effectiveness and sharing best practices based on real-world client projects.

Findings

Image clustering improves TAR efficiency for multimedia content

Real project data shows significant accuracy gains

Best practices enhance implementation success

Abstract

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.