James Z. Wang

Integrated Region-Based Image Retrieval

• Gebundenes Buch

Produktbeschreibung

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, itseemed 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.

Produktdetails

• The Information Retrieval Series 11
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-0-7923-7350-6
• 2001
• Seitenzahl: 196
• Erscheinungstermin: 31. Mai 2001
• Englisch
• Abmessung: 241mm x 160mm x 16mm
• Gewicht: 454g
• ISBN-13: 9780792373506
• ISBN-10: 0792373502
• Artikelnr.: 21643621

Inhaltsangabe

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.