Featured Chapters.- 1. Met: Image Mining for Typhoon Analysis.- 1.1. Introduction.- 1.2. Typhoon from an Informatics Perspective.- 1.3. Representation of the Typhoon.- 1.4. Image Mining.- 1.5. Image Mining Environment for Typhoon Analysis and Prediction.- 1.6. Conclusion.- 1.7. Acknowledgment.- 1.8. References.- 2. Discovering Patterns with and within Images.- 2.1. Introduction.- 2.2. Image Mining Techniques.- 2.3. Conclusion.- 2.4. References.- 3. A System Supporting Semantics Retrieval.- 3.1. Introduction.- 3.2. Scenery Analyzer: System Framework.- 3.3. A Hierarchical Representation for Low-Level Features.- 3.4. Extracting Semantic Features.- 3.5. Case Study of Semantic Features.- 3.6. Conclusion.- 3.7. References.- 4. Techniques for Color-Based Image Retrieval.- 4.1. Introduction.- 4.2. Color-Spaces.- 4.3. Color-based image description.- 4.4. Visual features extraction and representation.- 4.5. Distance Function.- 4.6. Similarity Search.- 4.7. Existing CBIR approaches.- 4.8. Open problems.- 4.9. Summary.- 4.10. Acknowledgment.- 4.11. References.- 5. Recovering in Video Documents.- 5.1. Introduction.- 5.2. Temporal video segmentation.- 5.3. Computation of optical flow.- 5.4. Building and selection of trajectories.- 5.5. Camera model.- 5.6. Recovery of camera motion without parallax.- 5.7. Recovery of camera motion with parallax.- 5.8. Integration.- 5.9. Conclusion.- 5.10. Acknowledgments.- 5.11. References.- 6. Mining of Video Database.- 6.1. Introduction.- 6.2. Semantics-Sensitive Video Database Model.- 6.3. Video Analysis and Feature Extraction.- 6.4. Semantics-Sensitive Video Classification.- 6.5. Hierarchical Database Indexing and Access.- 6.6. Conclusions.- 6.7. Acknowledgement.- 6.8. References.- 7. Medical Multimedia Databases.- 7.1. Introduction.- 7.2. Reviewof Medical Multimodality and Multimedia Systems.- 7.3. The Medimage System.- 7.4. The Epilepsy System.- 7.5. Conclusions.- 7.6. References.- 8. An Object Approach for Web Presentations.- 8.1. Introduction.- 8.2. The V-STORM System.- 8.3. The AROM System.- 8.4. Coupling AROM and V-STORM.- 8.5. The Template model.- 8.6. Related Works.- 8.7. Conclusion.- 8.8. References.- 9. Web Multiform Data Structuring.- 9.1. Introduction.- 9.2. Related work.- 9.3. UML conceptual model.- 9.4. XML logical model.- 9.5. XML physical model.- 9.6. Conclusion and future issues.- 9.7. References.- 10. Media Annotation.- 10.1. Introduction.- 10.2. Generation of describers.- 10.3. Dimensions.- 10.4. Querying.- 10.5. Conclusion.- 10.6. References.- 11. Audio Content-Based Classification.- 11.1. Introduction.- 11.2. Framework of semantic classes.- 11.3. Classification method.- 11.4. Retrieval.- 11.5. Experimentation.- 11.6. Comparison with related works.- 11.7. Conclusion.- 11.8. Acknowledgment.- 11.9. References.

Multimedia Mining: A Highway to Intelligent Multimedia Documents brings together experts in digital media content analysis, state-of-art data mining and knowledge discovery in multimedia database systems, knowledge engineers and domain experts from diverse applied disciplines.

Multimedia documents are ubiquitous and often required, if not essential, in many applications today. This phenomenon has made multimedia documents widespread and extremely large. There are tools for managing and searching within these collections, but the need for tools to extract hidden useful knowledge embedded within multimedia objects is becoming pressing and central for many decision-making applications. The tools needed today are tools for discovering relationships between objects or segments within multimedia document components, such as classifying images based on their content, extracting patterns in sound, categorizing speech and music, and recognizing and tracking objects in video streams.