Design and implementation of short video multi-labels generating based on content-aware dynamic graph convolutional networks

Linyue Ma; Mimi Zhang

doi:10.1117/12.3035497

19 July 2024 Design and implementation of short video multi-labels generating based on content-aware dynamic graph convolutional networks

Linyue Ma, Mimi Zhang

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133D (2024) https://doi.org/10.1117/12.3035497
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In recent years, short video platforms have gained prominence as a primary medium for communication. This paper delves into their application within the electric power industry. User-generated labels often fall short of effectively matching video content, providing limited guidance to both content reviewers and viewers. Moreover, manual content review heavily relies on human resources, necessitating a more intelligent approach to label generation. To address this, the paper introduces a Content-Aware Dynamic Graph Convolutional Network, harnessing artificial intelligence for precise multilabel generation in short videos. It begins by implementing an inter-frame absolute difference method with frame intervals to obtain keyframes. Subsequently, a content-aware dynamic graph convolutional network processes these frames, generating multiple labels rooted in content awareness. Ultimately, a set of key information labels is selected according to specific rules. This method significantly enhances label recognition accuracy, surpassing traditional approaches. Experimental results confirm its efficacy in multi-label recognition for short videos, promising advancements for the field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linyue Ma and Mimi Zhang "Design and implementation of short video multi-labels generating based on content-aware dynamic graph convolutional networks", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133D (19 July 2024); https://doi.org/10.1117/12.3035497

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