Multimedia Database Systems: Issues and Research Directions (Paperback, Softcover Repri)

Towards a Theory of Multimedia Database Systems.- 1. Introduction.- 2. Basic Ideas Underlying the Framework.- 3. Media Instances.- 3.1 The Clinton Example.- 3.2 Examples of Media-Instances.- 4. Indexing Structures and a Query Language for Multimedia Systems.- 4.1 Frame-Based Query Language.- 4.2 The Frame Data Structure.- 4.3 Query Processing Algorithms.- 4.4 Updates in Multimedia Databases.- 5. Multimedia Presentations.- 5.1 Generation of Media Events = Query Processing.- 5.2 Synchronization = Constraint Solving.- 5.3 Internal Synchronization.- 5.4 Media Buffers.- 6. Related Work.- 7. Conclusions.- A Unified Approach to Data Modelling and Retrieval for a Class of Image Database Applications.- 1. Introduction.- 2. Approaches to Image Data Modeling.- 2.1 Terminology.- 2.2 Conventional Data Models.- 2.3 Image Processing/Graphics Systems with Database Functionality.- 2.4 Extended Conventional Data Models.- 2.5 Extensible Data Models.- 2.6 Other Data Models.- 3. Requirements Analysis of Application Areas.- 3.1 A Taxonomy for Image Attributes.- 3.2 A Taxonomy for Retrieval Types.- 3.3 Art Galleries and Museums.- 3.4 Interior Design.- 3.5 Architectural Design.- 3.6 Real Estate Marketing.- 3.7 Face Information Retrieval.- 4. Logical Representations.- 5. Motivations for the Proposed Data Model.- 6. An Overview of AIR Framework.- 6.1 Data Model.- 6.2 The Proposed DBMS Architecture.- 7. Image Database Systems Based on AIR Model.- 8. Image Retrieval Applications Based on the Prototype Implementation of AIR Framework.- 8.1 Realtors Information System.- 8.2 Face Information Retrieval System.- 9. Research Issues in AIR Framework.- 9.1 Query Interface.- 9.2 Algorithms for RSC and RSS Queries.- 9.3 Relevance Feedback Modeling and Improving Retrieval Effectiveness.- 9.4 Elicitation of Semantic Attributes.- 10. Conclusions and Future Direction.- A. Image Logical Structures.- The QBISM Medical Image DBMS.- 1. Introduction.- 2. The Medical Application.- 2.1 Problem Definition.- 2.2 Data Characteristics.- 3. Logical Design.- 3.1 Data Types.- 3.2 Spatial Operations.- 3.3 Schema.- 3.4 Queries.- 4. Physical Database Design.- 4.1 Representation of a VOLUME.- 4.2 Representation of a REGION.- 4.3 Conclusions.- 5. System Issues.- 5.1 Starburst Extensions.- 5.2 System Architecture.- 6. Performance Experiments.- 6.1 Experimental Environment.- 6.2 Single-study Queries.- 6.3 Multi-study Queries.- 6.4 Results from the Performance Experiments.- 7. Conclusions and Future Work.- Retrieval of Pictures Using Approximate Matching.- 1. Introduction.- 2. Picture Representation.- 3. User Interface.- 4. Computation of Similarity Values.- 4.1 Similarity Functions.- 4.2 Object Similarities.- 4.3 Similarities of Non-spatial Relationships.- 4.4 Spatial Similarity Functions.- 5. Conclusion.- Ink as a First-Class Datatype in Multimedia Databases.- 1. Introduction.- 2. Ink as First-Class Data.- 2.1 Expressiveness of Ink.- 2.2 Approximate Ink Matching.- 3. Pictographic Naming.- 3.1 Motivation.- 3.2 A Pictographic Browser.- 3.3 The Window Algorithm.- 3.4 Hidden Markov Models.- 4. The ScriptSearch Algorithm.- 4.1 Definitions.- 4.2 Approaches to Searching Ink.- 4.3 Searching for Patterns in Noisy Text.- 4.4 The ScriptSearch Algorithm.- 4.5 Evaluation of ScriptSearch.- 4.6 Experimental Results.- 4.7 Discussion.- 5. Searching Large Databases.- 5.1 The HMM-Tree.- 5.2 The Handwritten Trie.- 5.3 Inter-character Strokes.- 5.4 Performance.- 6. Conclusions.- Indexing for Retrieval by Similarity.- 1. Introduction.- 2. Shape Matching.- 2.1 Rectangular Shape Covers.- 2.2 Storage Structure.- 2.3 Queries.- 2.4 Approximate Match.- 2.5 An Example.- 2.6 Experiment.- 3. Word Matching.- 4. Discussion.- Filtering Distance Queries in Image Retrieval.- 1. Introduction.- 2. Spatial Access Methods and Image Retrieval.- 2.1 Query Processor.- 2.2 Image Objects and Spatial Predicates.- 3. Snapshot.- 3.1 Regular Grid with Locational Keys.- 3.2 Clustering Technique.- 3.3 Extensible Hashing.- 3.4 Organiz