Intelligent Image Databases: Towards Advanced Image Retrieval addresses the image feature selection issue in developing content-based image retrieval systems. The book first discusses the four important issues in developing a complete content-based image retrieval system, and then demonstrates that image feature selection has significant impact on the remaining issues of system design. Next, it presents an in-depth literature survey on typical image features explored by contemporary content-based image retrieval systems for image matching and retrieval purposes. The goal of the survey is to determine the characteristics and the effectiveness of individual features, so as to establish guidelines for future development of content-based image retrieval systems. Intelligent Image Databases: Towards Advanced Image Retrieval describes the Advanced Region-Based Image Retrieval System (ARBIRS) developed by the authors for color images of real-world scenes. They have selected image regions for building ARBIRS as the literature survey suggests that prominent image regions, along with their associated features, provide a higher probability for achieving a higher level content-based image retrieval system. A major challenge in building a region-based image retrieval system is that prominent regions are rather difficult to capture in an accurate and error-free condition, particularly those in images of real-world scenes. To meet this challenge, the book proposes an integrated approach to tackle the problem via feature capturing, feature indexing, and database query. Through comprehensive system evaluation, it is demonstrated how these systematically integrated efforts work effectively to accomplish advanced image retrieval. Intelligent Image Databases: Towards Advanced Image Retrieval serves as an excellent reference and may be used as a text for advanced courses on the topic.

This book covers the principles and recent research results in intelligent image database systems design. Special emphasis is placed on spatial reasoning and the techniques for image indexing and retrieval, mainly based on the Theory of Symbolic Projection. In addition, applications of the theory and techniques to intelligent image database systems design are also discussed.

Multimedia databases address a growing number of commercially important applications such as media on demand, surveillance systems and medical systems. The book presents essential and relevant techniques and algorithms to develop and implement large multimedia database systems.The traditional relational database model is based on a relational algebra that is an offshoot of first-order logic and of the algebra of sets. The simple relational model is not powerful enough to address multimedia data. Because of this, multimedia databases are categorized into many major areas. Each of these areas are now so extensive that a major understanding of the mathematical core concepts requires the study of different fields such as information retrieval, digital image processing, feature extraction, fractals, machine learning, neuronal networks and high-dimensional indexing. This book unifies the essential concepts and recent algorithms into a single comprehensive volume.

As consumer costs for multimedia devices such as digital cameras and Web phones have decreased and diversity in the market has skyrocketed, the amount of digital information has grown considerably. Intelligent Multimedia Databases and Information Retrieval: Advancing Applications and Technologies details the latest information retrieval technologies and applications, the research surrounding the field, and the methodologies and design related to multimedia databases. Together with academic researchers and developers from both information retrieval and artificial intelligence fields, this book details issues and semantics of data retrieval with contributions from around the globe. As the information and data from multimedia databases continues to expand, the research and documentation surrounding it should keep pace as best as possible, and this book provides an excellent resource for the latest developments.

The explosive growth of multimedia data transmission has generated a critical need for efficient, high-capacity image databases, as well as powerful search engines to retrieve image data from them. This book brings together contributions by an international all-star team of innovators in the field who share their insights into all key aspects of image database and search engine construction. Readers get in-depth discussions of the entire range of crucial image database architecture, indexing and retrieval, transmission, display, and user interface issues. And, using examples from an array of disciplines, the authors present cutting-edge applications in medical imagery, multimedia communications, earth science, remote sensing, and other major application areas.

The World Wide Web and the Internet are signs that things will be very different in the future. And what is so striking about this computer-age future is that it comes incredibly fast and is incredibly overwhelming. Anyone who has surfed the Web has exclaimed at one point or another that there is so much information available, so much to search and so much to keep up with.Where Lycos and AltaVista are already accepted tools for textual information, image and multimedia search engines are the natural answers in the quest for pictorial information. This book provides a state-of-the-art description of that field. It contains the proceedings of a valuable workshop in Amsterdam, where people gathered to discuss the progress in the field. The topics cover computational methods of searching for pictures, the powerful pictorial clues in the recognition of objects, storage and indexing of objects in a database, and, ways to access the requested pictorial information.

The interaction of database and AI technologies is crucial to such applications as data mining, active databases, and knowledge-based expert systems. This volume collects the primary readings on the interactions, actual and potential, between these two fields. The editors have chosen articles to balance significant early research and the best and most comprehensive articles from the 1980s. An in-depth introduction discusses basic research motivations, giving a survey of the history, concepts, and terminology of the interaction. Major themes, approaches and results, open issues and future directions are all discussed, including the results of a major survey conducted by the editors of current work in industry and research labs. Thirteen sections follow, each with a short introduction. Topics examined include semantic data models with emphasis on conceptual modeling techniques for databases and information systems and the integration of data model concepts in high-level data languages, definition and maintenance of integrity constraints in databases and knowledge bases, natural language front ends, object-oriented database management systems, implementation issues such as concurrency control and error recovery, and representation of time and knowledge incompleteness from the viewpoints of databases, logic programming, and AI.

This volume is the first in a series which aims to contribute to the wider dissemination of the results of research and development in database systems for non-traditional applications and non-traditional machine organizations. It contains updated versions of selected papers from the First International Symposium on Database Systems for Advanced Applications.

The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.