An Evaluation Framework for Multidimensional Multimedia Descriptor Indexing

Abstract

Automatic multimedia retrieval requires the use of complex features, which are typically captured by multidimensional descriptors. A basic operation in a multimedia retrieval system is similarity computation, making use of descriptor-dependant metrics. Many data structures have been proposed for managing the representation of multidimensional descriptors, each geared towards efficiency in some set of basic operations.The paper describes a framework for evaluating multidimensional descriptor indexing structures and reports a set of experiments with selected descriptors indexing methods. The extensibility of the framework is illustrated by incorporating a recently-proposed structure, the BitMatrix. Data sets and experiment conditions can be set up so as to provide results that can be used in the choice of appropriate indexing structures for a class of multimedia retrieval applications.

Multimedia - A Multidisciplinary Approach to Complex Issues

“…Another set of experiments have been designed to observe the time performance of the BitMatrix as a memory-based indexing method. A specific framework for high-dimensional indexing evaluation has been used Gonçalves et al (2007), and the experiments were run on i9000 dataset. The MMDI allowed us to test a set of 5 methods: Sequential Scan, Bond, VA-File, GridBitmap, and BitMatrix.…”

High-Dimensional Indexing for Video Retrieval

“…A specific framework for high-dimensional indexing evaluation has been used Gonçalves et al (2007), and the experiments were run on i9000 dataset. The MMDI allowed us to test a set of 5 methods: Sequential Scan, Bond, VA-File, GridBitmap, and BitMatrix.…”