Integrated Region-Based Image Retrieval

Integrated Region-Based Image Retrieval by James Z. Wang.

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 mil...

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Bibliographic Details
Main Author: Wang, James Z. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2001.
Edition:1st ed. 2001.
Series:The Information Retrieval Series, 11
Springer eBook Collection.
Subjects:
Information storage and retrieval.
Data structures (Computer science).
Computers.
Optical data processing.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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