A Novel DC-GCN with Attention Mechanism for Accurate Near-Duplicate Video Data Cleaning
Jayalakshmi D
Jayalakshmi D
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Thandalam, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Thandalam, Chennai, Tamil Nadu, India.
There has been a steady emergence of nearly identical recordings in the last several decades, thanks to the exponential development of video data. The use of regular videos has been impacted by data quality difficulties produced by near-duplicate movies, which are becoming increasingly noticeable. While there has been progress in the field of near-duplicate video detection, there is still no automated merging method for video data characterised by high-dimensional features. As a result, it is challenging to automatically clean near-duplicate videos in advance video dataset data quality. Research on removing near-duplicate video data is still in its early stages. The precision of near-duplicate video data cleaning is severely compromised by the delicate issues of video data organization besides initial clustering centres in the current research, which arise when the previous distribution is unknown. In tackle these problems, we offer a new kind of Graph Convolutional Neural Network (GCN) that uses dense influences and a categorization attention mechanism. Deeply connected graph convolutional networks (DC-GCNs) learn about faraway nodes by making GCNs deeper. By using dense connections, the DC-GCN is able to multiplex the small-scale features of shallow layers and generate features at diverse scales. Finally, an attention mechanism is incorporated to aid in feature combination and importance determination. Sparrow Search Optimisation Algorithm (SSA) is used to pick the parameters of the given model in the most optimal way. In the end, experiments are carried out using a coal mining video dataset and a widely known dataset called CC_WEB_VIDEO. The simulation findings show that the suggested strategy performs better than certain previous studies.
Keywords
Video Data, Graph Convolutional Neural Network, Densely Connected Graph Convolutional Network, Sparrow Search Optimization Algorithm, Duplicate Video Data Cleaning.
P. Pei, X. Zhao, J. Li, Y. Cao, and X. Lai, “Vision Transformer-Based Video Hashing Retrieval for Tracing the Source of Fake Videos,” Security and Communication Networks, vol. 2023, pp. 1–16, Jun. 2023, doi: 10.1155/2023/5349392.
J.-M. Guo, A. W. H. Prayuda, H. Prasetyo, and S. Seshathiri, “Deep Learning-Based Image Retrieval With Unsupervised Double Bit Hashing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 11, pp. 7050–7065, Nov. 2023, doi: 10.1109/tcsvt.2023.3268091.
D. Hemanand, N. P. G. Bhavani, S. Ayub, M. W. Ahmad, S. Narayanan, and A. H, “Multilayer vectorization to develop a deeper image feature learning model,” Automatika, vol. 64, no. 2, pp. 355–364, Dec. 2022, doi: 10.1080/00051144.2022.2157946.
Y. Yang, H. Wang, J. Wang, K. Dong, and S. Ding, “Semantic-Preserving Surgical Video Retrieval With Phase and Behavior Coordinated Hashing,” IEEE Transactions on Medical Imaging, vol. 43, no. 2, pp. 807–819, Feb. 2024, doi: 10.1109/tmi.2023.3321382.
V. Srinivasan, V. H. Raj, A. Thirumalraj, and K. Nagarajan, “Detection of Data imbalance in MANET network based on ADSY-AEAMBi-LSTM with DBO Feature selection,” Journal of Autonomous Intelligence, vol. 7, no. 4, Jan. 2024, doi: 10.32629/jai.v7i4.1094.
L. Yuan et al., “Learnable Central Similarity Quantization for Efficient Image and Video Retrieval,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14, 2023, doi: 10.1109/tnnls.2023.3321148.
D. Lakshmi Narayana Reddy, R. Mahaveerakannan, S. Kumar, J. Chenni Kumaran, and M. Bhanurangarao, “A Structure for Forecasting Stomach Cancer Using Deep Learning and Advanced Tongue Characteristics,” Smart Trends in Computing and Communications, pp. 1–14, 2024, doi: 10.1007/978-981-97-1313-4_1.
P. Jing, H. Sun, L. Nie, Y. Li, and Y. Su, “Deep Multi-modal Hashing with Semantic Enhancement for Multi-label Micro-video Retrieval,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–12, 2024, doi: 10.1109/tkde.2023.3337077.
X. Gao, Z. Chen, B. Zhang, and J. Wei, “Deep Learning to Hash with Application to Cross-View Nearest Neighbor Search,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 1–1, 2024, doi: 10.1109/tcsvt.2023.3273400.
T. Yu, P. Mascagni, J. Verde, J. Marescaux, D. Mutter, and N. Padoy, “Live laparoscopic video retrieval with compressed uncertainty,” Medical Image Analysis, vol. 88, p. 102866, Aug. 2023, doi: 10.1016/j.media.2023.102866.
Z. Xi, X. Wang, and P. Cheng, “Unsupervised Hashing Retrieval via Efficient Correlation Distillation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 7, pp. 3529–3541, Jul. 2023, doi: 10.1109/tcsvt.2023.3234037.
Y. Huo et al., “Deep Semantic-Aware Proxy Hashing for Multi-Label Cross-Modal Retrieval,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 1, pp. 576–589, Jan. 2024, doi: 10.1109/tcsvt.2023.3285266.
C. Anitha, S. Srinivasulu Raju, R. Mahaveerakannan, A. Rajasekaran, and N. Pathak, “White Blood Cells Classification Using MBOA-Based MobileNet and Coupling Pre-trained Models with IFPOA,” Innovative Computing and Communications, pp. 573–588, 2024, doi: 10.1007/978-981-97-3588-4_46.
S. P. Jadhav, A. Srinivas, P. Dipak Raghunath, M. Ramkumar Prabhu, J. Suryawanshi, and A. H, “Deep learning approaches for multi-modal sensor data analysis and abnormality detection,” Measurement: Sensors, vol. 33, p. 101157, Jun. 2024, doi: 10.1016/j.measen.2024.101157.
K. Nithya and V. Rajamani, “Triplet Label Based Image Retrieval Using Deep Learning in Large Database,” Computer Systems Science and Engineering, vol. 44, no. 3, pp. 2655–2666, 2023, doi: 10.32604/csse.2023.027275.
L. Zhu, C. Zheng, W. Guan, J. Li, Y. Yang, and H. T. Shen, “Multi-Modal Hashing for Efficient Multimedia Retrieval: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 1, pp. 239–260, Jan. 2024, doi: 10.1109/tkde.2023.3282921.
K. Wu and L. Xu, “Deep Hybrid Neural Network With Attention Mechanism for Video Hash Retrieval Method,” IEEE Access, vol. 11, pp. 47956–47966, 2023, doi: 10.1109/access.2023.3276321.
W. Jo, G. Lim, G. Lee, H. Kim, B. Ko, and Y. Choi, “VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 3, pp. 2679–2687, Mar. 2024, doi: 10.1609/aaai.v38i3.28046.
P. Wu, J. Liu, X. He, Y. Peng, P. Wang, and Y. Zhang, “Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model,” IEEE Transactions on Image Processing, vol. 33, pp. 2213–2225, 2024, doi: 10.1109/tip.2024.3374070.
S. Silvia Priscila, S. K. Piramu Preethika, S. Radhakrishnan, R. Bagavathi Lakshmi, M. Sakthivanitha, and R. Mahaveerakannan, “Chaotic Map Cryptographic Hash-Blockchain Technology with Supply Chain Management,” Innovative Computing and Communications, pp. 599–612, 2024, doi: 10.1007/978-981-97-3588-4_48.
Y. Han, X. Yu, H. Luan, and J. Suo, “Event-Assisted Object Tracking on High-Speed Drones in Harsh Illumination Environment,” Drones, vol. 8, no. 1, p. 22, Jan. 2024, doi: 10.3390/drones8010022.
Y. Qin, O. Ye, and Y. Fu, “An Automatic Near-Duplicate Video Data Cleaning Method Based on a Consistent Feature Hash Ring,” Electronics, vol. 13, no. 8, p. 1522, Apr. 2024, doi: 10.3390/electronics13081522.
K. Aravinda, B. Santosh Kumar, B. P. Kavin, and A. Thirumalraj, “Traffic Sign Detection for Real-World Application Using Hybrid Deep Belief Network Classification,” Advanced Geospatial Practices in Natural Environment Resource Management, pp. 214–233, Mar. 2024, doi: 10.4018/979-8-3693-1396-1.ch011.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the
manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors would like to thank to the reviewers for nice comments on the
manuscript.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or
analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and
agreed to the published version of the manuscript.
Corresponding author
Jayalakshmi D
Jayalakshmi D
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Thandalam, Chennai, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution
NoDerivs is a more restrictive license. It allows you to redistribute the material commercially
or
non-commercially but the user cannot make any changes whatsoever to the original, i.e. no
derivatives of the
original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Jayalakshmi D, Hemavathi R, Murali L, Baskar Duraisamy, Banda SNV Ramana Murthy
and Sunita, “A Novel DC-GCN with Attention Mechanism for Accurate Near-Duplicate Video Data
Cleaning”, Journal of Machine and Computing, pp. 1001-1008, October 2024. doi:10.53759/7669/jmc202404093.
