Detecting Image Similarity Using SIFT

Sri, Kurra Hima; Manasa, Guttikonda Tulasi; Reddy, G. Greeshmanth; Bano, Shahina; Trinadh, Vempati Biswas

doi:10.1007/978-981-16-2126-0_45

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…The correlations between local image features are usually small, and the detection and matching of other features will not be affected when the image is aliased and obscured. SIFT (Sri et al, 2022; Xiong et al, 2021) and speeded up robust features (SURF; Raju et al, 2022) are the most widely used methods of extracting local features, since they can effectively capture invariance to rotation and scaling transformations and are robust to changes in illumination. Due to the complementarity of the global and local features, they are often used in combination.…”

Section: Computer Science Studiesmentioning

confidence: 99%

A survey of online video advertising

Zhang

Yan

et al. 2023

WIREs Data Min & Knowl

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With the development of social media and the ubiquity of the Internet, recent years have witnessed the rapid development of online video advertising among publishers and advertisers. Video advertising, as a new type of advertisement, has gained significant research attention from both academia and industry, coinciding with the ever-growing volume of online videos. In this research, we provide a comprehensive survey of online video advertising in the fields of social science and computer science. We investigate state-of-the-art articles from 1990 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the factors that cause advertising to affect people and the most popular video advertising techniques used in computer science. Finally, on the basis of the analytics of the surveyed papers, future challenges are identified and potential solutions to these are discussed.

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Section: Computer Science Studiesmentioning

confidence: 99%