Dataset And Case Studies For Visual Near-duplicates Detection In The Context Of Social Media

Abstract

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 the spread of such content. In this paper, we address this need by building a dataset of social media images and evaluating visual near-duplicates retrieval methods based on image retrieval and several advanced visual feature extraction methods. We evaluate the methods using a large-scale dataset of images we crawl from social media and their manipulated versions we generated, presenting promising results in terms of recall. We demonstrate the potential of this method in two case studies: one that shows the value of creating systems supporting manual content review, and another that demonstrates the usefulness of automatic large-scale data analysis.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymatatov2022dataset

Related papers

• Benchmarking Unsupervised Near-duplicate Image Detection (2019)10.85
• Efficient Discovery And Effective Evaluation Of Visual Perceptual Similarity: A Benchmark And Beyond (2023)4.52
• Dynamic Spatial Verification For Large-scale Object-level Image Retrieval (2019)0.00
• Benchmarking Pretrained Vision Embeddings For Near- And Duplicate Detection In Medical Images (2023)7.16
• Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale (2022)0.00
• CNN Retrieval Based Unsupervised Metric Learning For Near-duplicated Video Retrieval (2021)0.00
• Learning Non-metric Visual Similarity For Image Retrieval (2017)11.58
• Visual Link Retrieval And Knowledge Discovery In Painting Datasets (2020)12.25