Image Feature Extraction for Similarity Searching Using Transfer Learning with ResNet
This study evaluates the use of feature extraction with a pre-trained ResNet50 model for similarity search tasks. We employed transfer learning from both initial and intermediate layers of ResNet50 and applied a robust preprocessing approach, including resizing and Gaussian blur, to optimize feature...
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| Autores principales: | , , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/176283 |
| Aporte de: |
| Sumario: | This study evaluates the use of feature extraction with a pre-trained ResNet50 model for similarity search tasks. We employed transfer learning from both initial and intermediate layers of ResNet50 and applied a robust preprocessing approach, including resizing and Gaussian blur, to optimize feature extraction. The method achieved over 90% accuracy in nearest neighbor (NN) and k-Nearest Neighbors (k=3 and k=5) searches for both logos and paintings datasets. Notably, our approach does not require fine-tuning, which is advantageous when only one instance of each element is available. Overall, the method is effective and practical for similarity search applications. |
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