Content-Based Image And Video Retrieval
addresses the basic concepts and techniques for designing content-based image and video retrieval systems. It also discusses a variety of design choices for the key components of these systems. This book gives a comprehensive survey of the content-based image retrieval systems, including several content-based video retrieval systems. The survey includes both research and commercial content-based retrieval systems.
Content-Based Image And Video Retrieval
includes pointers to two hundred representative bibliographic references on this field, ranging from survey papers to descriptions of recent work in the area, entire books and more than seventy websites. Finally, the book presents a detailed case study of designing MUSE-a content-based image retrieval system developed at Florida Atlantic University in Boca Raton, Florida.
Series
Edition
Language
Place of publication
Target group
Professional and scholarly
College/higher education
Research
Edition type
Illustrations
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 17 mm
Weight
ISBN-13
978-1-4020-7004-4 (9781402070044)
DOI
10.1007/978-1-4615-0987-5
Schweitzer Classification
¿Oge Marques, PhD is Professor of Computer Science and Engineering at Florida Atlantic University. He is the author of eleven books and more than 120 refereed scholarly publications in the area of intelligent processing of visual information - which combines the fields of image processing, computer vision, (medical) image analysis, machine learning, AI, deep learning, and human visual perception.
Dr. Marques is a Senior Member of both the IEEE and the ACM, as well as a Sigma Xi Distinguished Speaker, a Fellow of the Leshner Leadership Institute of the American Association for the Advancement of Science (AAAS), Tau Beta Pi Eminent Engineer, and a member of the American Society for Engineering Education (ASEE), the American Association for the Advancement of Science (AAAS), and the honor societies of Sigma Xi, Phi Kappa Phi and Upsilon Pi Epsilon. Dr. Marques has more than thirty years ofteaching and research experience in eight countries, and has won several teaching awards; most recently the Engineering Educator of the Year Award, The Engineers' Council (2019).
1. Introduction.- 2. Fundamentals of Content-Based Image and Video Retrieval.- 1. Basic Concepts.- 2. A Typical CBIVR System Architecture.- 3. The User's Perspective.- 4. Summary.- 3. Designing a Content-Based Image Retrieval System.- 1. Feature Extraction and Representation.- 2. Similarity Measurements.- 3. Dimension Reduction and High-dimensional Indexing.- 4. Clustering.- 5. The Semantic Gap.- 6. Learning.- 7. Relevance Feedback (RF).- 8. Benchmarking CBVIR Solutions.- 9. Design Questions.- 10. Summary.- 4. Designing a Content-Based Video Retrieval System.- 1. The Problem.- 2. The Solution.- 3. Video Parsing.- 4. Video Abstraction and Summarization.- 5. Video Content Representation, Indexing, and Retrieval.- 6. Video Browsing Schemes.- 7. Examples of Video Retrieval Systems.- 8. Summary.- 5. A Survey of Content-Based Image Retrieval Systems.- 1. Introduction.- 2. Criteria.- 3. Systems.- 4. Summary and Conclusions.- 6. Case Study: Muse.- 1. Overview of the System.- 2. The User's Perspective.- 3. The RF Mode.- 4. The RFC Mode.- 5. Experiments and Results.- 6. Summary.- 7. Future Work.- References.
