Artificial intelligence for maximizing content based image retrieval

✍ Scribed by Zongmin Ma, Zongmin Ma

• Publisher: Information Science Reference
• Year: 2009
• Tongue: English
• Leaves: 451
• Series: Premier Reference Source
• Edition: 1
• Category: Library

✦ Synopsis

The increasing trend of multimedia data use is likely to accelerate creating an urgent need of providing a clear means of capturing, storing, indexing, retrieving, analyzing, and summarizing data through image data.

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

Title......Page 2
Table of Contents......Page 4
Detailed Table of Contents......Page 7
Preface......Page 15
Acknowledgment......Page 20
Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation......Page 22
Improving Image Retrieval by Clustering......Page 41
Review on Texture Feature Extraction and Desrciption Methods in Content-Based Medical Image Retrieval......Page 65
Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework......Page 89
Content Based Image Retrieval Using Active-Nets......Page 106
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View......Page 136
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints......Page 166
A Machine Learning-Based Model for Content-Based Image Retrieval......Page 192
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval......Page 213
Content Analysis from User’s Relevance Feedback for Content-Based Image Retrieval......Page 237
Preference Extraction in Image Retrieval......Page 256
Personalized Content-Based Image Retrieval......Page 282
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases......Page 310
Content-Based Retrieval for Mammograms......Page 336
Event Detection, Query, and Retrieval for Video Surveillance......Page 363
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework......Page 392