Article: Application of distributed artificial intelligence technology in key frame extraction of film and television video Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.41 pp.43 - 58 Abstract: Traditional video analysis relies on video frames, which often contain redundant data, making key frame extraction essential. However, existing methods frequently suffer from missing or redundant frames. To address this, this paper proposes a video key frame extraction method based on distributed artificial intelligence. First, mutual information between video frames is calculated. Then, SIFT feature points are extracted and transformed into polar coordinates, with each frame divided into sector regions to count feature points and compute inter-frame distances. To enhance precision, the CaffeNet model is adopted as a deep neural network to extract deep features using three training techniques. This approach significantly improves the accuracy of key frame extraction. Experimental results show that the proposed method achieves higher fidelity and compression rates than traditional techniques, and the extracted key frames align closely with reference standards without frame omission, demonstrating its effectiveness and robustness in real-world applications. Inderscience Publishers - linking academia, business and industry through research

Title: Application of distributed artificial intelligence technology in key frame extraction of film and television video

Authors: Feng Cheng

Addresses: School of Arts, Xi'an International Studies University, Xi'an, 710100, China

Abstract: Traditional video analysis relies on video frames, which often contain redundant data, making key frame extraction essential. However, existing methods frequently suffer from missing or redundant frames. To address this, this paper proposes a video key frame extraction method based on distributed artificial intelligence. First, mutual information between video frames is calculated. Then, SIFT feature points are extracted and transformed into polar coordinates, with each frame divided into sector regions to count feature points and compute inter-frame distances. To enhance precision, the CaffeNet model is adopted as a deep neural network to extract deep features using three training techniques. This approach significantly improves the accuracy of key frame extraction. Experimental results show that the proposed method achieves higher fidelity and compression rates than traditional techniques, and the extracted key frames align closely with reference standards without frame omission, demonstrating its effectiveness and robustness in real-world applications.

Keywords: distributed artificial intelligence technology; film and television video; key frame extraction; SIFT feature points.

DOI: 10.1504/IJICT.2025.149989

International Journal of Information and Communication Technology, 2025 Vol.26 No.41, pp.43 - 58

Received: 01 Aug 2025
Accepted: 26 Sep 2025
Published online: 20 Nov 2025 *

Title: Application of distributed artificial intelligence technology in key frame extraction of film and television video

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