Efficient Multimedia Similarity Measurement Using Similar Elements
This work addresses the challenge of efficient multimedia data retrieval for online social networking and large-scale systems, representing an incremental improvement in similarity measurement techniques.
The paper tackles the problem of image similarity measurement for large-scale multimedia retrieval by proposing a novel method (SMIN) and indexing structure (SMII) with an off-line index for reusable potential similar visual words, and demonstrates that their solution outperforms state-of-the-art methods on two real image datasets.
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.