Welcome to the InfoSci Platform
Reference Hub1
Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach

Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach

G. Megala, P. Swarnalatha, S. Prabu, R. Venkatesan, Anantharajah Kaneswaran
Copyright: © 2023 |Pages: 11
ISBN13: 9781668480984|ISBN10: 1668480980|ISBN13 Softcover: 9781668480991|EISBN13: 9781668481004
DOI: 10.4018/978-1-6684-8098-4.ch002
Cite Chapter Cite Chapter

MLA

Megala, G., et al. "Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach." Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT, edited by P. Swarnalatha and S. Prabu, IGI Global, 2023, pp. 18-28. https://doi.org/10.4018/978-1-6684-8098-4.ch002

APA

Megala, G., Swarnalatha, P., Prabu, S., Venkatesan, R., & Kaneswaran, A. (2023). Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach. In P. Swarnalatha & S. Prabu (Eds.), Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT (pp. 18-28). IGI Global. https://doi.org/10.4018/978-1-6684-8098-4.ch002

Chicago

Megala, G., et al. "Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach." In Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT, edited by P. Swarnalatha and S. Prabu, 18-28. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8098-4.ch002

Export Reference

Mendeley
Favorite

Abstract

Content-based video retrieval is a research field that aims to develop advanced techniques for automatically analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.