Integrated Region-Based Image Retrieval
Published on 31. May 2001
Book
Hardback
XIV, 178 pages
978-0-7923-7350-6 (ISBN)
Description
Content-based image retrieval is the set of techniques for retrieving relevant images from an image database on the basis of automatically derived image features. The need for efficient content-based image re trieval has increased tremendously in many application areas such as biomedicine, the military, commerce, education, and Web image clas sification and searching. In the biomedical domain, content-based im age retrieval can be used in patient digital libraries, clinical diagnosis, searching of 2-D electrophoresis gels, and pathology slides. I started my work on content-based image retrieval in 1995 when I was with Stanford University. The project was initiated by the Stan ford University Libraries and later funded by a research grant from the National Science Foundation. The goal was to design and implement a computer system capable of indexing and retrieving large collections of digitized multimedia data available in the libraries based on the media contents. At the time, it seemed reasonable to me that I should discover the solution to the image retrieval problem during the project. Experi ence has certainly demonstrated how far we are as yet from solving this basic problem.
More details
Series
Edition
2001 ed.
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Research
Illustrations
XIV, 178 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 16 mm
Weight
465 gr
ISBN-13
978-0-7923-7350-6 (9780792373506)
DOI
10.1007/978-1-4615-1641-5
Schweitzer Classification
Other editions
Additional editions
James Z. Wang
Integrated Region-Based Image Retrieval
Book
10/2012
Springer
€106.99
Shipment within 7-9 days
Content
1. Introduction.- 1. Text-based image retrieval.- 2. Content-based image retrieval.- 3. Applications of CBIR.- 4. Summary of our work.- 5. Structure of the book.- 6. Summary.- 2. Background.- 1. Introduction.- 2. Content-based image retrieval.- 3. Image semantic classification.- 4. Summary.- 3. Wavelets.- 1. Introduction.- 2. Fourier transform.- 3. Wavelet transform.- 4. Applications of wavelets.- 5. Summary.- 4. Statistical Clustering and Classification.- 1. Introduction.- 2. Artificial intelligence and machine learning.- 3. Statistical clustering.- 4. Statistical classification.- 5. Summary.- 5. Wavelet-Based Image Indexing and Searching.- 1. Introduction.- 2. Preprocessing.- 3. Multiresolution indexing.- 4. The indexing algorithm.- 5. The matching algorithm.- 6. Performance.- 7. Limitations.- 8. Summary.- 6. Semantics-Sensitive Integrated Matching.- 1. Introduction.- 2. Overview.- 3. Image segmentation.- 4. Image classification.- 5. The similarity metric.- 6. System for biomedical image databases.- 7. Clustering for large databases.- 8. Summary.- 7. Image Classification by Image Matching.- 1. Introduction.- 2. Industrial solutions.- 3. Related work in academia.- 4. System for screening objectionable images.- 5. Classifying objectionable websites.- 6. Summary.- 8. Evaluation.- 1. Introduction.- 2. Overview.- 3. Data sets.- 4. Query interfaces.- 5. Characteristics of IRM.- 6. Accuracy.- 7. Robustness.- 8. Speed.- 9. Summary.- 9. Conclusions and Future Work.- 1. Summary.- 2. Limitations.- 3. Areas of future work.- References.