Artificial Intelligence for Maximizing Content Based Image Retrieval

by Ma, Zongmin

• ISBN13: 9781605661742
• ISBN10: 1605661740
• Format: Hardcover
• Copyright: 2009-01-31
• Publisher: Igi Global

Summary

Artificial Intelligence for Maximizing Content Based Image Retrieval discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field. Providing state-of-the-art research from leading international experts, this book offers a theoretical perspective and practical solutions for academicians, researchers, and industry practitioners.

Table of Contents

Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation	p. 1
Improving Image Retrieval by Clustering	p. 20
Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval	p. 44
Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework	p. 68
Content Based Image Retrieval Using Active-Nets	p. 85
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View	p. 115
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints	p. 145
A Machine Learning-Based Model for Content-Based Image Retrieval	p. 171
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval	p. 192
Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval	p. 216
Preference Extraction in Image Retrieval	p. 235
Personalized Content-Based Image Retrieval	p. 261
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases	p. 289
Content-Based Retrieval for Mammograms	p. 315
Event Detection, Query, and Retrieval for Video Surveillance	p. 342
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework	p. 371
Compilation of References	p. 388
Index	p. 428
Table of Contents provided by Blackwell. All Rights Reserved.