A Review on Near Duplicate Detection of Images using Computer Vision Techniques
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.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionObject RecognitionSimilar Papers 제목 키워드 기반
Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Images
Near- and duplicate image detection is a critical concern in the field of medical imaging. Medical datasets often contain similar or duplicate images from various sources, which can lead to significant performance issues…
BenchmarkingRetrievalSpecificityTraining on test data: Removing near duplicates in Fashion-MNIST
MNIST and Fashion MNIST are extremely popular for testing in the machine learning space. Fashion MNIST improves on MNIST by introducing a harder problem, increasing the diversity of testing sets, and more accurately repr…
BIG-bench Machine LearningDiversityDataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media
The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena related to…
Image RetrievalRetrievalBenchmarking unsupervised near-duplicate image detection
Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descr…
BenchmarkingBinary ClassificationContent-Based Image RetrievalImage Forensics+3State of the Art: Image Hashing
Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and finding similar images from large-scale image content. The main challenge…
Image RetrievalRetrieval