Similarity-based ranking of videos from fixed-size one-dimensional video signature

The amount of information is multiplying, one of the popular and widely used formats is short videos. Therefore, maintaining the copyright protection of this information, preventing it from being disclosed without authorization, is a challenge. This work presents a way to rank a set of short videos...

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Bibliographic Details
Published in:Information retrieval (Boston) Vol. 27; no. 1; p. 25
Format: Journal Article
Language:English
Published: Dordrecht Springer Nature B.V 14.08.2024
Subjects:
Copy protection
Genetic algorithms
Neural networks
Similarity
Subgroups
Tensors
Video
ISSN:1386-4564, 1573-7659
Online Access:Get full text
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Summary:The amount of information is multiplying, one of the popular and widely used formats is short videos. Therefore, maintaining the copyright protection of this information, preventing it from being disclosed without authorization, is a challenge. This work presents a way to rank a set of short videos based on a video profile similarity metric, finding a set of reference videos, using a self-supervised method, without the need for human tagging. The self-supervised method uses a search based on a Genetic Algorithm, of a subgroup of the most similar videos. Similarities are calculated using the SMAPE metric on video signatures vectors, generated with a fixed size, using Structural Tensor, maximum sub matrix and T-SNE.
Bibliography:ObjectType-Article-1
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ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-024-09459-0