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DOI: https://doi.org/10.70117/hdujs.79.09.2025.828
Issue
Số 79-09.2025: Khoa học Tự nhiên, Kỹ thuật và Công nghệ
Section
Khoa học Tự nhiên và Công nghệ
How to Cite
Le, V. H., Le, D. N., & Nguyen, V. C. FEATURE ENGINEERING WITH CNN MODELS FOR PARTIAL VIDEO COPY DETECTION. Hong Duc University Journal of Science, 79(09). https://doi.org/10.70117/hdujs.79.09.2025.828

FEATURE ENGINEERING WITH CNN MODELS FOR PARTIAL VIDEO COPY DETECTION

Van Hao Le , Dinh Nghiep Le1, Van Cuong Nguyen1
1 Hong Duc University

Main Article Content

Abstract

2D convolutional neural networks are the key component in partial video copy detection systems. They play a crucial role in video retrieval and matching tasks within a large database. However, the performance characteristics of these feature extraction methods have been little discussed in the literature. This paper presents two key contributions. First, we conduct the experiments on a large-scale dataset to demonstrate the generalization capability and clarify the performance characteristics of popular neural networks. Next, we propose a time-series model approach to highlight the advantages and limitations of image features extracted from neural networks in the partial video copy detection problem.

Keywords

CNN models, feature engineering, partial video copy detection

Article Details

References

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