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A GCN-Based Framework for Generating Trailers

Authors:

Xuekun JiangAuthors Info & Claims

ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence

Pages 610 - 617

https://doi.org/10.1145/3532213.3532306

Published: 13 July 2022 Publication History

Abstract

The film-television industry continues to generate a large amount of information at all times, and the massive amount of movie data has promoted an increase in the demand for trailers. It has become a major research challenge to choose a movie of interest from the massive amount of movie data. This has driven the growth in demand for trailer production. Using computer technology to generate trailers automatically has two benefits: on the one hand, it can help people browse the content of a movie quickly and decide whether to pay for the movie; on the other hand, it can reduce the work of video creators and help them attract viewers with less cost. In this article, we construct a GCN-based convolution joint framework, which selects the trailer shots in the full-length movie according to the visual characteristics of the shots and the relationship between the shots. Firstly, the movie data is preprocessed for its division into sparse shots, followed by shot boundary detection and stratified sampling. Secondly, the visual features of shots are learned through multi-layer CNN. The topological relationship between shots is established by GCN to extract the features that include the shot relationship. These extracted features are then intelligently fused based on the assignment of different weights; shots with the fusion score higher than a certain threshold are selected for the trailer generation. The proposed framework is shown to other video summarization methods in the field of trailer generation.

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Cited By

Balestri RCascarano PEsposti MPescatore G(2024)An Automatic Deep Learning Approach for Trailer Generation through Large Language Models2024 9th International Conference on Frontiers of Signal Processing (ICFSP)10.1109/ICFSP62546.2024.10785516(93-100)Online publication date: 12-Sep-2024
https://doi.org/10.1109/ICFSP62546.2024.10785516
Montalvo-Lezama RMontalvo-Lezama BFuentes-Pineda G(2023)Improving Transfer Learning for Movie Trailer Genre Classification using a Dual Image and Video TransformerInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10334360:3Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103343

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ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence

March 2022

809 pages

ISBN:9781450396110

DOI:10.1145/3532213

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2022

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ICCAI '22

ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence

March 18 - 21, 2022

Tianjin, China

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Cited By

Balestri RCascarano PEsposti MPescatore G(2024)An Automatic Deep Learning Approach for Trailer Generation through Large Language Models2024 9th International Conference on Frontiers of Signal Processing (ICFSP)10.1109/ICFSP62546.2024.10785516(93-100)Online publication date: 12-Sep-2024
https://doi.org/10.1109/ICFSP62546.2024.10785516
Montalvo-Lezama RMontalvo-Lezama BFuentes-Pineda G(2023)Improving Transfer Learning for Movie Trailer Genre Classification using a Dual Image and Video TransformerInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10334360:3Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103343

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