Smart adaptive learning and optimized feature clustering for enhanced image retrieval
Abstract In recent years, Content-Based Image Retrieval (CBIR) has faced increasing challenges due to the rapid growth of multimedia content on the Internet. One of the primary difficulties in CBIR lies in identifying dissimilarities between various objects. This paper introduces a novel approach ca...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10375-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849343871415222272 |
|---|---|
| author | P. Umaeswari Sujata Patil Parameshachari Bidare Divakarachari Przemysław Falkowski-Gilski |
| author_facet | P. Umaeswari Sujata Patil Parameshachari Bidare Divakarachari Przemysław Falkowski-Gilski |
| author_sort | P. Umaeswari |
| collection | DOAJ |
| description | Abstract In recent years, Content-Based Image Retrieval (CBIR) has faced increasing challenges due to the rapid growth of multimedia content on the Internet. One of the primary difficulties in CBIR lies in identifying dissimilarities between various objects. This paper introduces a novel approach called SEGJO (Scaling Factor and Elite Opposition Learning-based Golden Jackal Optimization) for effective clustering of extracted features in CBIR. The inclusion of a Scaling Factor (SF) and Elite Opposition Learning (EOL) in SEGJO enhances its search capabilities and helps prevent premature convergence during the clustering process. CBIR extracts key features related to texture, shape, and color using techniques such as Local Binary Pattern, Zernike Moments, and Color Moments. Additionally, an Entropy-based Divergence (ED) function is incorporated into a Convolutional Neural Network (CNN) named EDCNN to improve matching accuracy by reducing redundant activation in hidden layers. The proposed SEGJO-EDCNN method is evaluated on the Corel 5 K and Oxford Flower datasets, and its performance is measured using precision, recall, F-score, mean average precision, and average recall. Comparative analysis with existing methods—ELNDP, SVM-CBIR, SPDNN, and DNN-SAR—demonstrates that SEGJO-EDCNN achieves a higher mean average precision of 97.595% on the Corel 5 K dataset, outperforming both ELNDP and DNN-SAR. Next, the MAP of SEGJO-EDCNN for the Oxford flower dataset is 99.239% which is higher compared to the SVM-CBIR. |
| format | Article |
| id | doaj-art-3bd2cc1b7e044d73a5cbf152230340db |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3bd2cc1b7e044d73a5cbf152230340db2025-08-20T03:42:49ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-10375-6Smart adaptive learning and optimized feature clustering for enhanced image retrievalP. Umaeswari0Sujata Patil1Parameshachari Bidare Divakarachari2Przemysław Falkowski-Gilski3Department of Computer Science and Business Systems R.M.K. Engineering CollegeDepartment of Electronics and Communication, KLE Technological UniversityDepartment of Electronics and Communication Engineering, Nitte Meenakshi Institute of TechnologyFaculty of Electronics, Telecommunications and Informatics, Gdansk University of TechnologyAbstract In recent years, Content-Based Image Retrieval (CBIR) has faced increasing challenges due to the rapid growth of multimedia content on the Internet. One of the primary difficulties in CBIR lies in identifying dissimilarities between various objects. This paper introduces a novel approach called SEGJO (Scaling Factor and Elite Opposition Learning-based Golden Jackal Optimization) for effective clustering of extracted features in CBIR. The inclusion of a Scaling Factor (SF) and Elite Opposition Learning (EOL) in SEGJO enhances its search capabilities and helps prevent premature convergence during the clustering process. CBIR extracts key features related to texture, shape, and color using techniques such as Local Binary Pattern, Zernike Moments, and Color Moments. Additionally, an Entropy-based Divergence (ED) function is incorporated into a Convolutional Neural Network (CNN) named EDCNN to improve matching accuracy by reducing redundant activation in hidden layers. The proposed SEGJO-EDCNN method is evaluated on the Corel 5 K and Oxford Flower datasets, and its performance is measured using precision, recall, F-score, mean average precision, and average recall. Comparative analysis with existing methods—ELNDP, SVM-CBIR, SPDNN, and DNN-SAR—demonstrates that SEGJO-EDCNN achieves a higher mean average precision of 97.595% on the Corel 5 K dataset, outperforming both ELNDP and DNN-SAR. Next, the MAP of SEGJO-EDCNN for the Oxford flower dataset is 99.239% which is higher compared to the SVM-CBIR.https://doi.org/10.1038/s41598-025-10375-6Content-Based image retrievalConvolutional neural networkElite opposition learningEntropy-based divergence functionGolden Jackal optimizationScaling factor |
| spellingShingle | P. Umaeswari Sujata Patil Parameshachari Bidare Divakarachari Przemysław Falkowski-Gilski Smart adaptive learning and optimized feature clustering for enhanced image retrieval Scientific Reports Content-Based image retrieval Convolutional neural network Elite opposition learning Entropy-based divergence function Golden Jackal optimization Scaling factor |
| title | Smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| title_full | Smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| title_fullStr | Smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| title_full_unstemmed | Smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| title_short | Smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| title_sort | smart adaptive learning and optimized feature clustering for enhanced image retrieval |
| topic | Content-Based image retrieval Convolutional neural network Elite opposition learning Entropy-based divergence function Golden Jackal optimization Scaling factor |
| url | https://doi.org/10.1038/s41598-025-10375-6 |
| work_keys_str_mv | AT pumaeswari smartadaptivelearningandoptimizedfeatureclusteringforenhancedimageretrieval AT sujatapatil smartadaptivelearningandoptimizedfeatureclusteringforenhancedimageretrieval AT parameshacharibidaredivakarachari smartadaptivelearningandoptimizedfeatureclusteringforenhancedimageretrieval AT przemysławfalkowskigilski smartadaptivelearningandoptimizedfeatureclusteringforenhancedimageretrieval |