Optimizing Image Retrieval With An Extended B-metric Space
Abdelkader Belhenniche, Roman Chertovskih . Arxiv 2024 – 0 citations
[Paper]
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}{\sigma}) 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}{\sigma}) 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}_{\sigma}) and ({\rm NEM}_r), adding dynamic and location-based factors to better capture details in images.