Efficient Multimedia Similarity Measurement Using Similar Elements

TL;DR

This paper introduces a new method for measuring image similarity efficiently in large-scale multimedia systems, utilizing a novel index structure and offline visual word indexing to enhance retrieval performance.

Contribution

The paper presents a novel similarity measurement method called SMIN and a new indexing structure SMI Temp Index (SMII), improving large-scale image retrieval efficiency.

Findings

Outperforms state-of-the-art methods in experiments

Efficient indexing structure reduces computation time

Offline visual word index enhances reusability

Abstract

Online social networking techniques and large-scale multimedia systems are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data. This trend has put forward higher requirements and greater challenges on massive multimedia data retrieval. In this paper, we investigate the problem of image similarity measurement which is used to lots of applications. At first we propose the definition of similarity measurement of images and the related notions. Based on it we present a novel basic method of similarity measurement named SMIN. To improve the performance of calculation, we propose a novel indexing structure called SMI Temp Index (SMII for short). Besides, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that our solution outperforms state-of-the-art method.