Optimizing Image Retrieval with an Extended b-Metric Space
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911221650817024 |
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| author | Belhenniche, Abdelkader Chertovskih, Roman |
| author_facet | Belhenniche, Abdelkader Chertovskih, Roman |
| contents | This article provides a new approach on how to enhance data storage and retrieval in the Query By Image Content Systems (QBIC) by introducing the ${\rm NEM}_σ$ distance measure, satisfying the relaxed triangle inequality. By leveraging the concept of extended $b$-metric spaces, we address complex distance relationships, thereby improving the accuracy and efficiency of image database management. The use of ${\rm NEM}_σ$ facilitates better scalability and accuracy in large-scale image retrieval systems, optimizing both the storage and retrieval processes. The proposed method represents a significant advancement over traditional distance measures, offering enhanced flexibility and precision in the context of image content-based querying. Additionally, we take inspiration from ice flow models using ${\rm NEM}_σ$ and ${\rm NEM}_r$, adding dynamic and location-based factors to better capture details in images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Image Retrieval with an Extended b-Metric Space Belhenniche, Abdelkader Chertovskih, Roman Optimization and Control Metric Geometry 68T10, 45E50, 54E35 This article provides a new approach on how to enhance data storage and retrieval in the Query By Image Content Systems (QBIC) by introducing the ${\rm NEM}_σ$ distance measure, satisfying the relaxed triangle inequality. By leveraging the concept of extended $b$-metric spaces, we address complex distance relationships, thereby improving the accuracy and efficiency of image database management. The use of ${\rm NEM}_σ$ facilitates better scalability and accuracy in large-scale image retrieval systems, optimizing both the storage and retrieval processes. The proposed method represents a significant advancement over traditional distance measures, offering enhanced flexibility and precision in the context of image content-based querying. Additionally, we take inspiration from ice flow models using ${\rm NEM}_σ$ and ${\rm NEM}_r$, adding dynamic and location-based factors to better capture details in images. |
| title | Optimizing Image Retrieval with an Extended b-Metric Space |
| topic | Optimization and Control Metric Geometry 68T10, 45E50, 54E35 |
| url | https://arxiv.org/abs/2411.18800 |