ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

AI-Based Video Summarization for Efficient Content Retrieval


Affiliations
1 Department of Computational Intelligence, SRM Institute of Science and Engineering, Kattankulathur Campus, India
2 Department of MCA, Jyoti Nivas College, India
3 Department of Electronics and Communication Engineering, ACE Engineering College, India
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, India
5 Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology Asmara, Eritrea
     

   Subscribe/Renew Journal


The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval.

Keywords

Video Summarization, DBN, Content Retrieval, RBF, Multimedia Content
Subscription Login to verify subscription
User
Notifications
Font Size

  • B.S. Tung and N.H. Thinh, “AI-Based Video Analysis for Traffic Monitoring”, Proceedings of Asia-Pacific Conference on Signal and Information Processing, pp. 2035-2040, 2022.

  • P. Narwal and K.K. Bhatia, “A Comprehensive Survey and Mathematical Insights Towards Video Summarization”, Journal of Visual Communication and Image Representation, Vol. 89, pp. 1-11, 2022.

  • A. Sabha and A. Selwal, “Data-Driven Enabled Approaches for Criteria-Based Video Summarization: A Comprehensive Survey, Taxonomy, and Future Directions”, Multimedia Tools and Applications, Vol. 78, pp. 61-75, 2023.

  • M. Tahir, B. Lee and M.N. Asghar, “Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities”, Cryptography, Vol. 7, No. 1, pp. 1-7, 2023.

  • L.J. Nixon, B. Philipp and R. Bocyte, “Content Wizard: Demo of a Trans-Vector Digital Video Publication Tool”, Proceedings of ACM International Conference on Interactive Media Experiences, pp. 296-298, 2021.

  • P.Y. Ingle and Y.G. Kim, “Multiview Abnormal Video Synopsis in Real-Time”, Engineering Applications of Artificial Intelligence, Vol. 123, pp. 1-14, 2023.

  • S. Selvi and V. Saravanan, “Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network”, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.

  • K. Muhammad and V.H.C. De Albuquerque, “Human Action Recognition using Attention based LSTM Network with Dilated CNN Features”, Future Generation Computer Systems, Vol. 125, pp. 820-830, 2021.

  • K. Asha, D. Anuradha and M. Rizvana, “Human Vision System Region of Interest Based Video Coding”, Compusoft, Vol 2, No. 5, pp. 127-134, 2013.

  • A. Sabha and A, Selwal, “Towards Machine Vision-Based Video Analysis in Smart Cities: A Survey, Framework, Applications and Open Issues”, Multimedia Tools and Applications, Vol. 87, 1-52, 2023.

  • L. Nixon and V. Mezaris, “Data-Driven Personalisation of Television Content: A Survey”, Multimedia Systems, Vol. 28, No. 6, pp. 2193-2225, 2022.

  • S. Gupta and K.S. Babu, “Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer Disease-Based Neurodegenerative Disorders”, Computational and Mathematical Methods in Medicine, Vol. 2022, pp. 1-11, 2022.

  • A.A. Khan, W. Ali and S. Tumrani, “Content-Aware Summarization of Broadcast Sports Videos: An Audio-Visual Feature Extraction Approach”, Neural Processing Letters, Vol. 52, pp. 1945-1968, 2020.

  • R.K. Nayak and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.

  • W.E.N. Zheng and S.A.T.O. Takuro, “Content-Oriented Common IoT Platform for Emergency Management Scenarios”, Proceedings of International Symposium on Wireless Personal Multimedia Communications, pp. 1-6, 2019.


Abstract Views: 181

PDF Views: 1




  • AI-Based Video Summarization for Efficient Content Retrieval

Abstract Views: 181  |  PDF Views: 1

Authors

Kaavya Kanagaraj
Department of Computational Intelligence, SRM Institute of Science and Engineering, Kattankulathur Campus, India
Shilpa Abhang
Department of MCA, Jyoti Nivas College, India
Julakanti Sampath Kumar
Department of Electronics and Communication Engineering, ACE Engineering College, India
R. K. Gnanamurthy
Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, India
V. Balaji
Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology Asmara, Eritrea

Abstract


The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval.

Keywords


Video Summarization, DBN, Content Retrieval, RBF, Multimedia Content

References