Enhancing image retrieval through optimal barcode representation
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| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 28847-28869 |
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1038/s41598-025-14576-x |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Khosrowshahli, Rasa |u Faculty of Mathematics and Science, Brock University, L2S 3A1, St. Catharines, ON, Canada (ROR: https://ror.org/056am2717) (GRID: grid.411793.9) (ISNI: 0000 0004 1936 9318) | |
| 245 | 1 | |a Enhancing image retrieval through optimal barcode representation | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Data binary encoding has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding, generating barcodes from deep learning feature extraction for image retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting high-dimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and remains an unresolved problem. Difference-based binarization of features is one of the most efficient binarization methods, transforming continuous feature vectors into binary sequences and capturing trend information. However, the performance of this method is highly dependent on the ordering of the input features, leading to a significant combinatorial challenge. This research addresses this problem by optimizing feature sequences based on retrieval performance metrics. Our approach identifies optimal feature orderings, leading to substantial improvements in retrieval effectiveness compared to arbitrary or default orderings. We assess the performance of the proposed approach in various medical and non-medical image retrieval tasks. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as COVID-19 Chest X-rays dataset. In addition, we evaluate the proposed approach on non-medical benchmark image datasets, such as CIFAR-10, CIFAR-100, and Fashion-MNIST. Our findings demonstrate the importance of optimizing binary barcode representation to significantly enhance accuracy for fast image retrieval across a wide range of applications, highlighting the applicability and potential of barcodes in various domains. | |
| 653 | |a Deep learning | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Bar codes | ||
| 653 | |a Image retrieval | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Medical research | ||
| 653 | |a Methods | ||
| 653 | |a Inventory control | ||
| 653 | |a Data processing | ||
| 653 | |a Performance assessment | ||
| 653 | |a COVID-19 | ||
| 653 | |a Inventory management | ||
| 653 | |a Inventory | ||
| 653 | |a Retrieval performance measures | ||
| 653 | |a Semantics | ||
| 653 | |a Information retrieval | ||
| 653 | |a Machine learning | ||
| 700 | 1 | |a Kheiri, Farnaz |u Faculty of Engineering and Applied Sciences, University of Ontario Institute of Technology, L1G 0C5, Oshawa, ON, Canada (ROR: https://ror.org/016zre027) (GRID: grid.266904.f) (ISNI: 0000 0000 8591 5963) | |
| 700 | 1 | |a Asilian Bidgoli, Azam |u Faculty of Science, Wilfrid Laurier University, N2L 3C5, Waterloo, ON, Canada (ROR: https://ror.org/00fn7gb05) (GRID: grid.268252.9) (ISNI: 0000 0001 1958 9263) | |
| 700 | 1 | |a Tizhoosh, H. R. |u Kimia Lab, Mayo Clinic, 55905, Rochester, MN, USA (ROR: https://ror.org/02qp3tb03) (GRID: grid.66875.3a) (ISNI: 0000 0004 0459 167X) | |
| 700 | 1 | |a Makrehchi, Masoud |u Faculty of Engineering and Applied Sciences, University of Ontario Institute of Technology, L1G 0C5, Oshawa, ON, Canada (ROR: https://ror.org/016zre027) (GRID: grid.266904.f) (ISNI: 0000 0000 8591 5963) | |
| 700 | 1 | |a Rahnamayan, Shahryar |u Department of Engineering, Brock University, L2S 3A1, St. Catharines, ON, Canada (ROR: https://ror.org/056am2717) (GRID: grid.411793.9) (ISNI: 0000 0004 1936 9318) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 28847-28869 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3237114627/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
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