AI-driven video summarization for optimizing content retrieval and management through deep learning techniques
Abstract With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87824-9 |
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| author | Deepali Vora Payal Kadam Dadaso D Mohite Nilesh Kumar Nimit Kumar Pratheeik Radhakrishnan Shalmali Bhagwat |
| author_facet | Deepali Vora Payal Kadam Dadaso D Mohite Nilesh Kumar Nimit Kumar Pratheeik Radhakrishnan Shalmali Bhagwat |
| author_sort | Deepali Vora |
| collection | DOAJ |
| description | Abstract With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system. |
| format | Article |
| id | doaj-art-f91fd21341d44359a01d46ad192dfb0e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f91fd21341d44359a01d46ad192dfb0e2025-02-09T12:32:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-87824-9AI-driven video summarization for optimizing content retrieval and management through deep learning techniquesDeepali Vora0Payal Kadam1Dadaso D Mohite2Nilesh Kumar3Nimit Kumar4Pratheeik Radhakrishnan5Shalmali Bhagwat6Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Bharati Vidyapeeth (Deemed to be University) College of EngineeringSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University)Abstract With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system.https://doi.org/10.1038/s41598-025-87824-9Video summarizationContent retrievalConvolutional neural networksLSTMResNet50 |
| spellingShingle | Deepali Vora Payal Kadam Dadaso D Mohite Nilesh Kumar Nimit Kumar Pratheeik Radhakrishnan Shalmali Bhagwat AI-driven video summarization for optimizing content retrieval and management through deep learning techniques Scientific Reports Video summarization Content retrieval Convolutional neural networks LSTM ResNet50 |
| title | AI-driven video summarization for optimizing content retrieval and management through deep learning techniques |
| title_full | AI-driven video summarization for optimizing content retrieval and management through deep learning techniques |
| title_fullStr | AI-driven video summarization for optimizing content retrieval and management through deep learning techniques |
| title_full_unstemmed | AI-driven video summarization for optimizing content retrieval and management through deep learning techniques |
| title_short | AI-driven video summarization for optimizing content retrieval and management through deep learning techniques |
| title_sort | ai driven video summarization for optimizing content retrieval and management through deep learning techniques |
| topic | Video summarization Content retrieval Convolutional neural networks LSTM ResNet50 |
| url | https://doi.org/10.1038/s41598-025-87824-9 |
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