Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405029672