2023
doi: 10.12720/jait.14.4.668-673
Fusion of CNN-QCSO for Content Based Image Retrieval
Sarva Naveen Kumar1,*
,
- 1Department of Electronics and Communication Engineering, Vasavi College of Engineering, Hyderabad, India
- 2Department of Electronics and Communication Engineering, GITAM School of Technology, Gandhi Institute of Technology and Management (Deemed to Be University), Visakhapatnam, India
- Manuscript receivedOctober 23, 2022
- revisedNovember 16, 2022
- acceptedFebruary 6, 2023
- publishedJuly 19, 2023
Abstract
As the growth of digital images is been widely increased over the last few years on internet, the retrieval of required image is been a big problem. In this paper, a combinational approach is designed for retrieval of image form big data. The approach is CNN-QCSO, one is deep learning technique, i.e., Convolutional Neural Network (CNN) and another is optimization technique, i.e., Quantm Cuckoo Search Optimization (QCSO). CNN is used for extracting of features for the given query image and optimization techniques helps in achieving the global best features by changing the internal parameters of processing layers. The Content Based Image Retrieval (CBIR) is proposed in this study. In big data analysis, CNN is vastly used and have many applications like identifying objects, medical imaging fields, security analysis and so on. In this paper, the combination of two efficient techniques helps in identifying the image and achieves good results. The results shows that CNN alone achieves an accuracy of 94.8% and when combined with QCSO the rate of accuracy improved by 1.6%. The entire experimental values are evaluated using matlab tool.
Keywords
- Content Based Image Retrieval (CBIR)
- Convolutional Neural Networks (CNN)
- cuckoo search optimization
- Quantum Cuckoo Search Optimization (QCSO)
How to Cite
Sarva Naveen Kumar and Ch. Sumanth Kumar, "Fusion of CNN-QCSO for Content Based Image Retrieval," Journal of Advances in Information Technology, vol. 14, no. 4, pp. 668-673, 2023. https://doi.org/10.12720/jait.14.4.668-673
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
