2020/01/06 by K. K. Thyagharajan, G. Kalaiarasi
Computer Science · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer science #Computer vision #Currency Recognition and Detection #Digital Media Forensic Detection #Digital image #Feature (linguistics) #Feature detection (computer vision) #Feature extraction #Field (mathematics) #Image (mathematics) #Image processing #Information retrieval #Object (grammar) #Object detection #Pattern recognition (psychology) #The Internet #Transformation (genetics) #World Wide Web #cs.CV
paper · pdf · doi:10.1007/s11831-020-09400-w
37 Pages, 7 figures, "For online first version, see https://link.springer.com/article/10.1007/s11831-020-09400-w"
openalex publication_date 2020/01/06 · openalex created_date 2020/01/23 · arxiv created 2020/09/07 · arxiv updated 2020/09/08 · openalex updated_date 2026/08/05
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.