Content-Based Image and Video Retrieval Based on Hybrid Feature Extraction Techniques
Keywords:
Content Based Image Retrieval (CBIR), Recursive Neural Network (RNN). Latent Semantic Indexing (LSI), Image Retrieval (IR)Abstract
The system uses image and video segmentation to improve accuracy and loss metrics. The semantic gap between CBIR content and visual qualities is addressed in the study. Hybrid feature extraction strategies may help content-based retrieval systems close this gap. This study presents a hybrid feature extraction approach to Content-Based Image and Video Retrieval (CBIVR). The study examines video segmentation as well as retrieval to improve video searches and give meaningful abstractions. The study uses keyframes to automatically extract important frames from video diaries. An effective segmentation technique removes the backdrop from input data from the HMDB along with CIFAR-10 datasets. Feature selection-based optimization reduces input variables, improving model performance and computing effort. Using rank-listed numerical properties of phenomena, a hybrid feature vector conditions and machine learning model create informed estimates. For better training data analysis, the study uses error-learning ResNets, an artificial neural network having hundreds of layers that have feed-forward connections. Phase retrieval utilizing diffracted intensity distribution recreates the sample's object plane phase shift. The loss function evaluates the author's machine learning system. Retraining the system with updated authority with pre-trained models in post-processing optimizes video retrieval. The finding opens the door to more sophisticated multimedia material retrieval applications in numerous sectors. The system obtains higher accuracy and loss metrics by utilizing image and video segmentation. Impressive performance is shown by the picture segmentation model in training, with a loss of 0.1466 and an accuracy of 94%, while competitive results are maintained on the test set, with a loss of 0.67 and an accuracy of 82.46%. The model achieves a video loss of 0.39 and a video accuracy of 90% while being trained for video segmentation. These encouraging findings demonstrate the promise of the hybrid approach in improving content-based retrieval systems, opening the door for further investigation into cutting-edge segmentation algorithms, novel training datasets, and cutting-edge deep learning architectures to revolutionize multimedia content retrieval in a wide range of contexts.
Downloads
References
Khokher, Amandeep, and Rajneesh Talwar. "Content-based image retrieval: Feature extraction techniques and applications." In International conference on recent advances and future trends in information technology (iRAFIT2012), pp. 9-14. 2012.
Rehman, M., Iqbal, M., Sharif, M. and Raza, M., “Content-based image retrieval: a survey.” World Applied Sciences Journal,19(3), pp.404-412, 2012. Lakshmi R. Nair, Kamalraj Subramaniam,
G. K. D. Prasanna Venkatesan, · P. S. Baskar, T. Jayasankar, “Essentiality for bridging the gap between low and semantic level features in image retrieval systems: an overview.” J Ambient Intell Human Compute, 2020. J. Jayanthi · E. Laxmi Lydia · N. Krishna raj ·
T. Jayasankar · R. Lenin Babu · R. Adaline Suji, “An effective deep learning features based integrated framework for iris detection and recognition,” J Ambient Intell Human Compute, 2020.
Ashraf, R., Ahmed, M., Jabbar, S., Khalid, S., Ahmad, A., Din, S. and Jeon, G., “Content- based image retrieval by using color descriptor and discrete wavelet transform.
Journal of medical systems,” 42(3), p.44, 2018.
Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N.I., Zafar, B., Dar, S.H., Sajid, M. and Khalil, T., “Content-based image retrieval and feature extraction: a comprehensive review.” Mathematical Problems in Engineering, 2019.
Baig, F., Mehmood, Z., Rashid, M., Javid, M.A., Rehman, A., Saba, T. and Adnan, A.,” Boosting the performance of the sBoVW model using SURF–CoHOG-based sparse features with relevant feedback for CBIR.” Iranian Journal of Science and Technology, Transactions of Electrical Engineering,44(1), pp.99-118, 2020.
S. Yang, L. Li, S. Wang, W. Zhang, Q. Huang, and Q. Tian, “SkeletonNet: a hybrid network with a skeleton-embedding process for multi-view image representation learning,” IEEE Transactions on Multimedia, vol. 1, no. 1, 2019.
W. Zhao, L. Yan, and Y. Zhang, “Geometric- constrained multi-view image matching method based on semi-global optimization,” Geo-Spatial Information Science, vol. 21, no. 2, pp. 115–126, 2018.
W. Zhou, H. Li, and Q. Tian, “Recent advance in content-based image retrieval: a literature survey,” 2017.
A. Amelio, “A new axiomatic methodology for image similarity,” Applied Soft Computing, vol. 81, p. 105474, 2019.
S. Susan, P. Agrawal, M. Mittal, and S. Bansal, “New shape descriptor in the context of edge continuity,” CAAI Transactions on Intelligence Technology, vol. 4, no. 2, pp. 101–109, 2019.
L. Piras and G. Giacinto, “Information fusion in content-based image retrieval: a comprehensive overview,” Information Fusion, vol. 37, pp. 50–60, 2017.
L. Amelio and A. Amelio, “Classification methods in image analysis with a special focus on medical analytics,” in Machine Learning Paradigms, pp. 31–69, Springer, Basel, Switzerland, 2019.
Madhu, and Raman Kumar. "A hybrid feature extraction technique for content- based medical image retrieval using segmentation and clustering techniques." Multimedia Tools and Applications 81, no. 6 pp: 8871-8904, 2022.
Bhaumik, Hrishikesh, Siddhartha Bhattacharyya, Mausumi Das Nath, and Susanta Chakraborty. "Hybrid soft computing approaches to content-based video retrieval: A brief review." Applied Soft Computing 46, 2016.
Palai, Charulata, Pradeep Kumar Jena, Satya Ranjan Pattanaik, Trilochan Panigrahi, and Tapas Kumar Mishra. "Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion." International Journal of Advanced Computer Science and Applications 14, no. 3, 2023.
Reddy, Tatireddy Subba, K. Sanjeevaiah, Sajja Karthik, Mahesh Kumar, and D. Vivek. "Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimizati Algorithm." International Journal of Software Innovation (IJSI) 11, no. 1, pp: 1-15, 2023.
Kusi-Duah, Samuel, Obed Appiah, and Peter Appiahene. "An Improved and Efficient Content-Based Medical Image Retrieval Technique vs State-of-Art Texture Feature Extraction Techniques." Journal of BioMed Research and Reports 2, no. 4, 2023.
Devulapalli, Sudheer, Anupama Potti, Rajakumar Krishnan, and Md Sameeruddin Khan. "Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques." Materials Today: Proceedings , 2021.
Alsmadi, M.K., “Content-Based Image Retrieval Using Color, Shape, and Texture Descriptors and Features.” Arabian Journal for Science and Engineering, pp.1-14, 2020.
Chhabra, P., Garg, N.K. and Kumar, M., “Content-based image retrieval system using ORB and SIFT features.” Neural Computing and Applications, 32(7), pp.2725-2733, 2020.
Wang, Fengyuan, Jianhua Lv, Guode Ying, Shenghui Chen, and Chi Zhang. "Facial expression recognition from image based on hybrid features understanding." Journal of Visual Communication and Image Representation 59, pp: 84-88, 2019.
Singh, Aman, Amit Dixit, and Brajesh Kumar Singh. "Hypertuned Convolutional Neural Network Residual Model Based Content-Based Image Retrieval System." International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), pp. 139-144. IEEE, 2023.
Khan, Umer Ali, Ali Javed, and Rehan Ashraf. "An effective hybrid framework for content-based image retrieval (CBIR)." Multimedia Tools and Applications 80, 2021.
Ansari, Aasif, and Muzammil H. Mohammed. "Content-based video retrieval systems- methods, techniques, trends, and challenges." International Journal of Computer Applications 112, no. 7, 2015.
Hamed Nassar , Ahmed Taha , T M Nazmy, Khaled Ahmed Nagaty. “Retrieving of video scenes using arabic closed-caption.” International Journal of Intelligent computing and Information Systems (IJICIS), pp: 191-203, 2008.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
