Image Database Indexing Techniques

View Sample PDF

Author(s): Michael Vassilakopoulos (University of Central Greece, Greece), Antonio Corral (University of Almería, Spain), Boris Rachev (Technical University of Varna, Bulgaria), Irena Valova (University of Rousse, Bulgaria)and Mariana Stoeva (Technical University of Varna, Bulgaria)
Copyright: 2009
Pages: 8
Source title: Handbook of Research on Geoinformatics
Source Author(s)/Editor(s): Hassan A. Karimi (University of Pittsburgh, USA)
DOI: 10.4018/978-1-59140-995-3.ch003

Keywords: Environment & Agriculture / Environmental Science and Technologies / Geoinformatics / Information Science Reference

Purchase

View Image Database Indexing Techniques on the publisher's website for pricing and purchasing information.

Abstract

Image Databases (IDBs) are a kind of Spatial Databases where a large number of images are stored and queried. In this chapter, techniques for indexing an IDB for efficiently processing several kinds of queries, like retrieval based on features, content, structure, processing of joins, and queries by example are reviewed. The main indexing techniques used in IDBs are either members of the R-tree family (data driven structures), or members of the quadtree family (space driven structures). Although, research on IDB indexing counts several years, there are still significant research challenges, which are also discussed in this chapter. IDBs and their indexing structures bring together two different disciplines (databases and image processing) and interdisciplinary research efforts are required. Moreover, dealing with the semantic gap (successful integrated retrieval based on low-level features and high-level semantic features) and querying between images and other kinds of spatial data are also significant future research directions.

The IRMA Community

Research IRM

Image Database Indexing Techniques

Purchase

Abstract

Related Content

IRMA Sponsors