Optimizing Image Retrieval with an Extended b-Metric Space

TL;DR

This paper introduces a new extended b-metric space-based distance measure, ${\rm NEM}_{\sigma}$, to improve accuracy and scalability in large-scale image retrieval systems within QBIC frameworks.

Contribution

It presents a novel distance measure, ${\rm NEM}_{\sigma}$, satisfying relaxed triangle inequality, enhancing image retrieval accuracy and efficiency over traditional methods.

Findings

Improved image retrieval accuracy and scalability.

Enhanced flexibility in distance measurement.

Better handling of complex image relationships.

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 ${\rm NEM}_{\sigma}$ 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.