An up–to–date, expert guide to modern digital image databases
This volume presents the state of the art in digital image database design, with a concentration on storage and retrieval techniques, and includes a set of selected application case studies.
Chapters by experts from around the world explore a variety of techniques for accessing images based on color, texture, shape, and semantic descriptions. Underlying principles are stressed, including compression, indexing, storage organization, and transmission.
Image Databases also features detailed coverage of these important issues:
- Hierarchical storage management
- Database support
- Search at multiple abstraction levels
- Content extraction from compressed imagery
- Standards for representation and search
Case studies cover important application areas including:
- Photographic images
- Satellite imagery
- Images in the oil industry
- Medical imagery
A wide range of introductory material and an extensive bibliography makes Image Databases an excellent text for graduate–level students. It also serves as a valuable reference for developers and researchers in the field, and as a guide for helping IT professionals more fully understand the discipline.
Digital Imagery: Fundamentals (V. Castelli & L. Bergman).
Visible Image Retrieval (C. Colombo & A. Del Bimbo).
Satellite Imagery in Earth Science Applications (H. Ramapriyan).
Medical Imagery (S. Wong & K. Hoo).
Images in the Exploration for Oil and Gas (P. Tilke).
STORAGE AND SYSTEM ARCHITECTURE.
Storage Architectures for Digital Imagery (H. Vin).
Database Support for Multimedia Applications (M. Ortega–Binderberger and K. Chakrabarti).
Image Compression––A Review (S. Hemami).
Transmission of Digital Imagery (J. Percival).
INDEXING AND RETRIEVAL.
Introduction to Content–Based Image Retrieval–Overview of Key Techniques (Y. Li and C. Kuo).
Color for Image Retrieval (J. Smith).
Texture Features for Image Retreival (B. Manjunath & W. Ma).
Shape Representation for Image Retrieval (B. Kimia).
Multidimensional Indexing Structures for Content–Based Retrieval (V. Castelli).
Multimedia Indexing (C. Faloutsos).
Compressed or Progressive Image Search (S. Panchanathan).
Concepts and Techniques for Indexing Visual Semantics (A. Jaimes & S. Chang).
VITTORIO CASTELLI received his MS in both statistics and electrical engineering and his PhD in electrical engineering from Stanford University. He currently works at the IBM Thomas J. Watson Research Center, where his main research interests include information theory, statistics, classification, and their applications to performance analysis and computer architecture.
LAWRENCE D. BERGMAN received his PhD in Computer Science from the University of North Carolina at Chapel Hill, and currently works at the IBM Thomas J. Watson Research Center. His research interests include user–interfaces and visualization tools for content–based retrieval, and application development environments for pervasive computing.