Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets
A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the tr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/404821 |
| _version_ | 1797206713819987968 |
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| author | Siyu Lai Qinghua Yang Wenjin He Yuanzhong Zhu Juan Wang |
| author_facet | Siyu Lai Qinghua Yang Wenjin He Yuanzhong Zhu Juan Wang |
| author_sort | Siyu Lai |
| collection | DOAJ |
| description | A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples. |
| first_indexed | 2024-04-24T09:11:24Z |
| format | Article |
| id | doaj.art-44f9aa0ad84c42859887f1f3cb465c84 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:11:24Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-44f9aa0ad84c42859887f1f3cb465c842024-04-15T17:46:26ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-012941236124610.17559/TV-20210925093644Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale DatasetsSiyu Lai0Qinghua Yang1Wenjin He2Yuanzhong Zhu3Juan Wang4Department of Medical Imaging, North Sichuan Medical College, ChinaDepartment of Medical Imaging, North Sichuan Medical College, ChinaDepartment of Medical Imaging, North Sichuan Medical College, ChinaDepartment of Medical Imaging, North Sichuan Medical College, China1) College of Computer Science, China West Normal University, China 2) Department of Computer and Information Sciences, Temple University, USA College of Computer Science, China West Normal University, Shida Road, Nanchong, 637002, Sichuan Province, ChinaA vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples.https://hrcak.srce.hr/file/404821Bayes classifierimage retrievalrelevance feedbacksupport vector machine classifiertransfer learning |
| spellingShingle | Siyu Lai Qinghua Yang Wenjin He Yuanzhong Zhu Juan Wang Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets Tehnički Vjesnik Bayes classifier image retrieval relevance feedback support vector machine classifier transfer learning |
| title | Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets |
| title_full | Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets |
| title_fullStr | Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets |
| title_full_unstemmed | Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets |
| title_short | Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets |
| title_sort | image retrieval method combining bayes and svm classifier based on relevance feedback with application to small scale datasets |
| topic | Bayes classifier image retrieval relevance feedback support vector machine classifier transfer learning |
| url | https://hrcak.srce.hr/file/404821 |
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