A Multimedia Data Management Approach with GeM-Tree

Abstract

In this paper, we propose a multimedia data management framework using GeM-Tree. GeM-Tree is a multidimensional tree-based index structure which provides a generalized framework to organize and retrieve images and videos seamlessly. In addition to supporting different multimedia data types and diverse representations, the proposed data management framework supports varied multimedia retrieval strategies like content-based image and video retrieval, mixed multimedia data retrieval where cross-similarity between images and videos is considered, region-based retrieval, etc. The framework embeds a high-level semantic relationship between multimedia data objects with a construct called Hierarchical Markov Model Mediator via a novel affinity promotion technique to improve query result relevance manifold. Extensive experiments were conducted with different multimedia data types possessing varied representations, different retrieval approaches, and different data sizes. The encouraging results demonstrate a potential solution to the important and complex issue of managing a large volume of multimedia data.