# Use Image Clustering to Facilitate Technology Assisted Review

arXiv: https://arxiv.org/abs/2112.08604 · HTML: https://mlsanity.com/paper/2112.08604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Empirical Study of Deep Learning for Text Classification in Legal Document Review](https://mlsanity.com/paper/1904.01723/) (2019)
- [Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness](https://mlsanity.com/paper/1904.01718/) (2019)
- [Image Analytics for Legal Document Review: A Transfer Learning Approach](https://mlsanity.com/paper/1912.12169/) (2019)

## Abstract (arXiv)

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.