Tree-Based Indexes for Image Data (1998)
| Venue: | Journal of Visual Communication and Image Representation, Volume 9, Number |
| Citations: | 5 - 2 self |
BibTeX
@ARTICLE{Brown98tree-basedindexes,
author = {Leonard Brown and Le Gruenwald},
title = {Tree-Based Indexes for Image Data},
journal = {Journal of Visual Communication and Image Representation, Volume 9, Number},
year = {1998},
volume = {4},
pages = {300--313}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
As in conventional DataBase Management Systems (DBMSs), to allow users to efficiently access and retrieve data objects, a MultiMedia DataBase Management System (MMDBMS) must employ an effective access method such as indexing and hashing. This paper provides a survey of treebased multidimensional indexing techniques for MMDBMSs that maintain image data represented as feature vectors. These techniques support such data while maintaining desirable characteristics of a Btree, an index structure most commonly used in traditional DBMSs. In this survey, we provide descriptions of each tree as well as give examples of the different data organization schemes. We also describe the advantages and disadvantages of using each technique. In addition, we provide classifications of the trees using several different properties. These classifications should assist researchers in identifying the strengths and weaknesses of any new indexing technique they develop as well as help users determine the most appropriate data structure for their applications. 1.
