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

TL;DR

This paper reviews computer vision techniques for detecting near-duplicate images, highlighting current methods, challenges, and future research directions to improve image search and management.

Contribution

It provides a comprehensive survey of state-of-the-art approaches and feature extraction methods for near duplicate image detection, filling a gap in existing literature.

Findings

Summarizes key computer vision-based approaches

Identifies main challenges in near duplicate detection

Suggests future research directions

Abstract

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.