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Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach
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Author(s): G. Megala (Vellore Institute of Technology, India), P. Swarnalatha (Vellore Institute of Technology, India), S. Prabu (Pondicherry University, India), R. Venkatesan (SASTRA University, India)and Anantharajah Kaneswaran (University of Jaffna, Sri Lanka)
Copyright: 2023
Pages: 11
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch002
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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.
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