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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

A deep learning-based highlight detection with pairwise segment ranking for effective video summarization

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pp. 2165–2177Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1780XML
Received:
08 May 2024
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1780
Pages:
2165–2177

Abstract

In the modern era, the sheer volume of video content available poses significant challenges for viewers trying to extract critical information swiftly. Addressing this issue, our research introduces an innovative Highlight Detection and Pairwise Ranking Model for video summarization. This paper delves into the design and evaluation of a hybrid model that effectively identifies and ranks video segments, producing concise and coherent video summaries. By integrating deep learning techniques for highlight detection with pairwise ranking, our model assesses not only the individual importance of each segment but also its relevance in relation to others. Through extensive experiments on diverse datasets, we demonstrate that our approach consistently outperforms existing state-of-the-art video summarization techniques in terms of both user satisfaction and objective measures. Our findings indicate a promising direction for the efficient summarization of long-form videos, catering to the evolving needs of contemporary audiences.

Keywords

Subject Classifications

68M2068T07

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