  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.
