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.

Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.

Multimedia technologies are rapidly attracting more and more interest every day. The Internet as seen from the end user is one of the reasons for this phenomenon, but not the only one. Video on Demand is one of the buzzwords today, but its real availability to the general public is yet to come. Content providers – such as publishers, broadcasting companies, and audio/video production ?rms – must be able to archive and index their productions for later retrieval. This is a formidable task, even more so when the material to be sorted encompasses many di?erent types of several media and covers a time span of several years. In order for such a vast amount of data to be easily available, existing database design models and indexing methodologies have to be improved and re?ned. In addition, new techniques especially tailored to the various types of multimedia must be devised and evaluated. For archiving and trasmission, data compression is another issue that needs to be addressed. In many cases, it has been found that compression and indexing can be successfully integrated, since compressing the data by ?ltering out irrelevancy implies some degree of und- standing of the content structure.

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 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.

Perceptual processes in humans and machines, investigated and simulated by means of the computational approach, are the subject matter of this volume. Researchers in artificial intelligence, pattern recognition, and psychology discuss aspects of vision, speech understanding, sensory-motor coordination, and their interplay with cognitive and behavioral functionalities. The papers adopt the computational approach as the basic research paradigm. Connectionist models, numerical and statistical techniques, symbolic (logic-based) formalisms, and hybrid representations provide the formal background to the research. Some of the papers were prepared for a workshop held in Trieste, Italy, in October 1992.