Intelligent Image Database Systems

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. Contents: Spatial Reasoning, Image Indexing and Retrieval Using Symbolic Projections (S K Chang et al.)Iconic Indexing by 2D Strings (S K Chang et al.)A Normalized Index for Image Databases (G Petraglia et al.)Representation of 3D Symbolic and Binary Pictures Using 3D Strings (S K Chang & Y Li)Spatial Knowledge Representation for Iconic Image Database (S-Y Lee & F-J Hsu)Ordering Information and Symbolic Projection (C Schlieder)Determination of the Views of a Moving Agent by Means of Symbolic Projections (E Jungert)A Logical Framework for Spatio Temporal Indexing of Image Sequences (A Del Bimbo & E Vicario)A Generalized Approach to Image Indexing and Retrieval Based on 2D Strings (E G M Petrakis & S C Orphanoudakis)Using 2D-String Decision Trees for Symbolic Picture Retrieval (D J Buehrer et al.)Efficient Image Retrieval Algorithms for Large Spatial Databases (J C R Tseng et al.)A Prototype Multimedia Database System Incorporating Iconic Indexing (T Arndt & R Kuppanna)A Two and Three Dimensional Ship Database Application (J Hildebrandt & K Tang) Readership: Researchers and practitioners in computer science. keywords:

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.

Database management is attracting wide interest in both academic and industrial contexts. New application areas such as CAD/CAM, geographic information systems, and multimedia are emerging. The needs of these application areas are far more complex than those of conventional business applications. The purpose of this book is to bring together a set of current research issues that addresses a broad spectrum of topics related to database systems and applications. The book is divided into four parts: - object-oriented databases, - temporal/historical database systems, - query processing in database systems, - heterogeneity, interoperability, open system architectures, multimedia database systems.

This book presents recent research in intelligent information and database systems. The carefully selected contributions were initially accepted for presentation as posters at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) held from to 5 April 2017 in Kanazawa, Japan. While the contributions are of an advanced scientific level, several are accessible for non-expert readers. The book brings together 47 chapters divided into six main parts: • Part I. From Machine Learning to Data Mining.• Part II. Big Data and Collaborative Decision Support Systems,• Part III. Computer Vision Analysis, Detection, Tracking and Recognition,• Part IV. Data-Intensive Text Processing,• Part V. Innovations in Web and Internet Technologies, and• Part VI. New Methods and Applications in Information and Software Engineering. The book is an excellent resource for researchers and those working in algorithmics, artificial and computational intelligence, collaborative systems, decision management and support systems, natural language processing, image and text processing, Internet technologies, and information and software engineering, as well as for students interested in such research areas.

With the rapid growth in the use of computers to manipulate, process, and reason about multimedia data, the problem of how to store and retrieve such data is becoming increasingly important. Thus, although the field of multimedia database systems is only about 5 years old, it is rapidly becoming a focus for much excitement and research effort. Multimedia database systems are intended to provide unified frameworks for requesting and integrating information in a wide variety of formats, such as audio and video data, document data, and image data. Such data often have special storage requirements that are closely coupled to the various kinds of devices that are used for recording and presenting the data, and for each form of data there are often multiple representations and multiple standards - all of which make the database integration task quite complex. Some of the problems include: - what a multimedia database query means - what kinds of languages to use for posing queries - how to develop compilers for such languages - how to develop indexing structures for storing media on ancillary devices - data compression techniques - how to present and author presentations based on user queries. Although approaches are being developed for a number of these problems, they have often been ad hoc in nature, and there is a need to provide a princi pled theoretical foundation.

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.