An identification framework for print-scan books in a large database

Lee, Sang Hoon

doi:10.1016/j.ins.2017.02.001

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Considering the large number of photos and videos that have not yet been detected on the web, it is very inefficient to capture all of the infringements with human labor. An automated copyright photo identification method is required to efficiently capture these copyright infringements [3][4][5][6] by finding a perfectly identical original photo the same as an query image. When a subject is photographed at different angle at the same time, the photos are not identical but very similar, and it is a very challenging task to distinguish these photos considering the trade-off between speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Photo Identification Framework to Prevent Copyright Infringement with Manipulations

et al. 2021

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In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of infringements has been increasing continuously. To uncover these infringements and handle big data extracted from copyright photos, we propose a photo copyright identification framework to accurately handle manipulations of stolen photos. From a collage of cropped photos, regions of interest (RoIs) are detected to reduce the influence of cropping and identify each photo by Image RoI Detection. Binary descriptors for quick database search are generated from the RoIs by Image Hashing robustly to geometric and color manipulations. The matching results of Image Hashing are verified by measuring their similarity using the proposed Image Verification to reduce false positives. Experimental results demonstrate that the proposed framework outperforms other image retrieval methods in identification accuracy and significantly reduces the false positive rate by 2.8%. This framework is expected to identify copyright infringements in practical situations and have a positive effect on the copyright market.

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Section: Introductionmentioning

confidence: 99%