# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 6 Conclusions  
This study proves that, by using pre-trained DL models, better features can be extracted. This can provide a better result in terms of precision concerning the manual feature descriptors derived by traditional methods such as SIFT, GLCM, wavelet, LBP, LTQ, etc. This work proposes a novel CBIR technique based on transfer learning to retrieve images from the massive database using DL and ML models. The image retrieval method of the proposed system consists of two modules. Feature extraction from the images is performed in the first module using pre-trained models ResNet50 and VGG16 to lower the computational cost. In the second module, features from the query image and features retrieved from database images are compared, and the most similar images are presented in response.  
The effectiveness of the suggested CBIR approach is assessed on images from the natural-image dataset, fruits 360 dataset, and coral images using the evaluation metric precision. The proposed model was evaluated on the Caltech 101 dataset and exhibited exceptional performance in certain classes, such as airplanes, motorbikes, cars, and elephants. Additionally, the model demonstrated high precision on the beach class of the WANG dataset. Experimental findings show that, in comparison to existing methods, the suggested CBIR method is more accurate and quick, and the suggested system has significant retrieving capabilities. The suggested technique works better than the image retrieval system and achieves a 100% precision rate on a huge database with more than 10,000 images. However, one limitation of the features extracted using pre-trained deep learning models is that the system's retrieval outcomes may not be good if we query similar images from the database with images at various orientation angles. A rotation-invariant CBIR system can be introduced in the future, which can be the next improvement step.  
Methodology, S.S.; Validation, R.M.; Investigation, R.M. and A.A.; Data curation, R.M.; Writing-original draft, S.S.; Writing-review & editing, A.A.; Supervision, R.M. and A.A. All authors have read and agreed to the published version of the manuscript.  
This research project was supported by a grant from the \"Research Center of College of Computer and Information Sciences\", Deanship of Scientific Research, King Saud University.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Year** & **Dataset Used** & **\%Precision** \\ \hline [PERSON] et al. [59] & 2017 & WANG & 94.3 \\ [PERSON] et al. [37] & 2018 & WANG & 87.5 \\ [PERSON] et al. [31] & 2018 & WANG & 73.5 \\ [PERSON] et al. [29] & 2018 & Corel & 88.83 \\ [PERSON] et al. [27] & 2018 & Corel & 87.85 \\ [PERSON] et al. [54] & 2018 & Coil20, corel1k & 96 \\ [PERSON] et al. [32] & 2019 & Corel, Caltech 256 & 84.39 \\ [PERSON] et al. [53] & 2019 & Oxford5k, paris5K, flicker1M & 97.32 \\ [PERSON] et al. [60] & 2019 & Caltech101, corel1K & 76.5 \\ [PERSON] et al. [61] & 2019 & WANG, Holidays & 89.5 \\ [PERSON] et al. [57] & 2020 & Corel1K & 92.2 \\ [PERSON] et al. [62] & 2021 & WANG & 80.3 \\ [PERSON] et al. [58] & 2021 & Imagedb2000, Caltech101, corel1K & 96.12 \\ [PERSON] et al. [55] & 2022 & Corel1K, Corel5K, caltech256 & 99.4 \\ [PERSON] et al. [39] & 2022 & Corel, VOC & 96.03 \\ [PERSON] et al. [42] & 2022 & Corel10K, CAFIR10 & 98.1 \\ [PERSON] et al. [38] & 2023 & Corel1K & 86.06 \\ [PERSON] et al. [56] & 2023 & OT, caltech101, corel1K & 98.78 \\
**The Proposed System** & **-** & **Corel, Natural, fruits 360** & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of precision with existing techniques.  
Data Availability Statement: Data sharing does not apply to this article as authors have used publicly available datasets, whose details are included in the \"experimental results and discussions\" section of this article. Please contact the authors for further requests.  
The authors declare no conflict of interest.