Towardsa Theory of Multimedia Database Systems Sherry Marcus and V.S. Subrahmanian 1
1．Introduction 1
2．Basic Ideas Underlying the Framework 3
3．Media Instances 4
3．1 The Clinton Example 5
3．2 Examples of Media-Instances 8
4．Indexing Structures and a Query Language for Multimedia Systems 12
4．1 Frame-Based Query Language 12
4．2 The Frame Data Structure 15
4．3 Query Processing Algorithms 21
4．4 Updates in Multimedia Databases 22
5．1 Generation of Media Events = Query Processing 24
5．Multimedia Presentations 24
5．2 Synchronization = Constraint Solving 27
5．3 Internal Synchronization 28
5．4 Media Buffers 28
6．Related Work 29
7．Conclusions 31
A Unified Approach to Data Modelling and Retrieval for a Class of Image Database Applications 37
Venkat N Gudivada Vijay V Raghavan and Kanonluk Vanapipat 37
1．Introduction 37
2．Approaches to Image Data Modeling 39
2．1 Terminology 40
2．2 Conventional Data Models 40
2．3 Image Processing/Graphics Systems with Database Functionality 41
2．5 Extensible Data Models 42
2．4 Extended Conventional Data Models 42
3．Requirements Analysis of Application Areas 43
3．1 A Taxonomy for Image Attributes 43
2．6 Other Data Models 43
3．2 A Taxonomy for Retrieval Types 46
3．3 Art Galleries and Museums 48
3．4 Interior Design 48
3．5 Architectural Design 49
3．6 Real Estate Marketing 49
3．7 Face Information Retrieval 50
4．Logical Representations 51
5．Motivations for the Proposed Data Model 52
6．1 Data Model 53
6．An Overview of AIR Framework 53
6．2 The Proposed DBMS Architecture 57
7． Image Database Systems Based on AIR Model 58
8．Image Retrieval Applications Based on the Prototype Implementation of AIR Framework 61
8．1 Realtors Information System 61
8．2 Face Information Retrieval System 62
9．Research Issues in AIR Framework 64
9．1 Query Interface 64
9．2 Algorithms for RSC and RSS Queries 67
9．3 Relevance Feedback Modeling and Improving Retrieval Effectiveness 68
9．4 Elicitation of Semantic Attributes 69
10．Conclusions and Future Direction 70
A．Image Logical Structures 74
The QBISM Medical Image DBMS Manish Arya, William Cody, Christos Faloutsos, Joel Richardson, and Art 79
1．Introduction 79
2．The Medical Application 81
2．1 Problem Definition 81
2．2 Data Characteristics 82
3．Logical Design 83
3．1 Data Types 83
3．2 Spatial Operations 83
3．3 Schema 84
3．4 Queries 85
4．Physical Database Design 86
4．1 Representation of a VOLUME 87
4．2 Representation of a REGION 88
4．3 Conclusions 89
5．System Issues 89
5．1 Starburst Extensions 89
5．2 System Architecture 90
6．Performance Experiments 93
6．1 Experimental Environment 93
6．2 Single-study Queries 94
6．3 Multi-study Queries 96
6．4 Results from the Performance Experiments 97
7．Conclusions and Future WorR 97
A. Prasad Sistla and Clement Yu 101
1．Introduction 101
Retrieval of Pictures Using Approximate Matching 101
2．Picture Representation 102
3．User Interface 103
4．Computation of Similarity Values 105
4．1 Similarity Functions 105
4．2 Object Similarities 106
4．3 Similarities of Non-spatial Relationships 107
4．4 Spatial Similarity Functions 108
5．Conclusion 111
Ink as a First- Class Datatype in Multimedia Databases Walid G. Aref, Daniel Barbara. and Daniel Lop 113
1．Introduction 113
2．Ink as First- Class Data 114
2．1 Expressiveness of Ink 115
2．2 Approximate Ink Matching 116
3．Pictographic Naming 117
3．1 Motivation 118
3．2 A Pictographic Browser 120
3．3 The Window Algorithm 121
3．4 Hidden Markov Models 122
4．The ScriptSearch Algorithm 124
4．1 Definitions 125
4．2 Approaches to Searching Ink 126
4．3 Searching for Patterns in Noisy Text 128
4．4 The ScriptSearch Algorithm 129
4．5 Evaluation of ScriptSearch 132
4．6 Experimental Results 134
4．7 Discussion 136
5．1 The HMM-Tree 137
5．Searching Large Databases 137
5．2 The Handwritten Trie 149
5．3 Inter-character Strokes 160
5．4 Performance 160
6．Conclusions 160
Indexing for Retrieval by Similarity H.V. Jagadish 165
1．Introduction 165
2．Shape Matching 166
2．1 Rectangular Shape Covers 167
2．2 Storage Structure 169
2．3 Queries 171
2．4 Approximate Match 172
2．5 An Example 177
2．6 Experiment 178
3．Word Matching 180
4．Discussion 181
Filtering Distance Queries in Image Retrieval 185
A. Belussi, E. Bertino. A. Biavasco, and S. Rizzo 185
1．Introduction 185
2．Spatial Access Methods and Image Retrieval 187
2．1 Query Processor 187
2．2 Image Objects and Spatial Predicates 189
3．Snapshot 191
3．1 Regular Grid with Locational Keys 192
3．2 Clustering Technique 194
3．3 Extensible Hashing 195
3．4 Organization of Snapshot 198
4．Filtering Metric Queries with Snapshot 201
4．1 Search Algorithm 202
4．2 Min Algorithm 205
5．Optimization of Spatial Queries 210
6．Conclusions and Future Work 211
Stream-based Versus Structured Video Objects: Issues, Solutions, and Challenges Shahram Ghandehariza 215
1．Introduction 215
2．Stream-based Presentation 217
2．1 Continuous Display 218
2．2 Pipelining to Minimize Latency Time 224
2．3 High Bandwidth Objects and Scalable Servers 225
2．4 Challenges 226
3．Structured Presentation 227
3．1 Atomic Object Layer 229
3．2 Composed Object Layer 231
3．3 Challenges 232
4．Conclusion 235
The Storage and Retrieval of Continuous Media Data Banu Ozden. Rajeev Rastogi and Avi Silberschatz 237
1．Introduction 237
2．Retrieving Continuous Media Data 238
3．Matrix-Based Allocation 240
3．1 Storage Allocation 241
3．2 Buffering 243
3．3 Repositioning 243
3．4 Implementation of VCR Operations 244
4．Variable Disk Transfer Rates 245
5．Horizontal Partitioning 247
5．1 Storage Allocation 248
5．2 Retrieval 250
6．Vertical Partitioning 250
6．1 Size of Buffers 252
6．2 Data Retrieval 255
7．Related Work 257
8．Research Issues 257
8．1 Load Balancing and Fault Tolerance Issues 257
8．2 Storage Issues 258
8．3 Data Retrieval Issues 259
9．Concluding Remarks 260
1．Introduction 263
Querying Multimedia Databases in SQL Sherry Marcus 263
2．Automobile Multimedia Database Example 265
3．Logical Query Language 269
4．Querying Multimedia Databases in SQL 270
5．Expressing User Requests in SQL 274
6．Conclusions 276
Multimedia Authoring Systems Ross Cutler and Kasim Selcuk Candan 279
1．Introduction 279
2．Underlying Technology 280
2．1 ODBC 280
2．2 OLE 281
2．3 DDE 281
2．4 DLL 281
3．Sample Application- Find-Movie 282
2．5 MCI 282
4．Multimedia Toolbook 3.0 283
5．IconAuthor 6.0 287
6．Director 4.0 289
7．MAS s and Current Technology 290
7．1 How to improve MAS s? 291
7．2 How to Benefit from MAS s in Multimedia Research 294
8．Conclusion 295
Metadata for Building the Multimedia Patch Quilt Vipul Kashyap, Kshitij Shah, and Amit Sheth 297
1．Introduction 297
2．Characterization of the Ontology 300
2．1 Terminological Commitments: Constructing an Ontology 301
2．2 Controlled Vocabulary for Digital Media 302
2．3 Better understanding of the query 304
2．4 Ontology Guided Extraction of Metadata 305
3．Construction and Design of Metadata 306
3．1 Classification of Metadata 307
3．2 Meta-correlation: The Key to Media-Independent Semantic Correlation 309
3．3 Extractors for Metadata 312
3．4 Storage of Metadata 314
4．Association of Digital Media Data with Metadata 315
4．1 Association of Metadata with Image Data 315
4．2 Association of Symbolic Descriptions with Image Data 316
4．3 Metadata for Multimedia Objects 316
5．Conclusion 317
Contributors 321