# A Review on Near Duplicate Detection of Images using Computer Vision Techniques

arXiv: https://arxiv.org/abs/2009.03224 · HTML: https://mlsanity.com/paper/2009.03224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Pre-NeRF 360: Enriching Unbounded Appearances for Neural Radiance Fields](https://mlsanity.com/paper/2303.12234/) (2023)
- [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://mlsanity.com/paper/2202.09020/) (2022)
- [Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections](https://mlsanity.com/paper/2410.19437/) (2024)
- [Data Leakage in Automotive Perception: Practitioners' Insights](https://mlsanity.com/paper/2604.06899/) (2026)

## Abstract (arXiv)

Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.