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A novel keyframe extraction method for video classification using deep neural networks

Savran Kızıltepe, Rukiye and Gan, John Q and Escobar, Juan José (2021) 'A novel keyframe extraction method for video classification using deep neural networks.' Neural Computing and Applications. ISSN 0941-0643

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Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.

Item Type: Article
Uncontrolled Keywords: Deep learning; Convolutional neural networks; Recurrent neural networks; Keyframe extraction; Video classification
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 10 Aug 2021 15:19
Last Modified: 15 Jan 2022 01:38

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