Intelligent Multimedia Databases And Information Retrieval

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.

"This book details the latest information retrieval technologies and applications, the research surrounding the field, and the methodologies and design related to multimedia databases"--Provided by publisher.

The Center for Intelligent Information Retrieval (CIIR) was formed in the Computer Science Department ofthe University ofMassachusetts, Amherst in 1992. The core support for the Center came from a National Science Foun- tion State/Industry/University Cooperative Research Center(S/IUCRC) grant, although there had been a sizeable information retrieval (IR) research group for over 10 years prior to that grant. Thebasic goal ofthese Centers is to combine basic research, applied research, and technology transfer. The CIIR has been successful in each of these areas, in that it has produced over 270 research papers, has been involved in many successful government and industry collaborations, and has had a significant role in high-visibility Internet sites and start-ups. As a result of these efforts, the CIIR has become known internationally as one of the leading research groups in the area of information retrieval. The CIIR focuses on research that results in more effective and efficient access and discovery in large, heterogeneous, distributed, text and multimedia databases. The scope of the work that is done in the CIIR is broad and goes significantly beyond “traditional” areas of information retrieval such as retrieval models, cross-lingual search, and automatic query expansion. The research includes both low-level systems issues such as the design of protocols and architectures for distributed search, as well as more human-centered topics such as user interface design, visualization and data mining with text, and multimedia retrieval.

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. Contents:IntroductionMultimedia DatabasesTransform FunctionsCompressionFeature ExtractionLow Dimensional IndexingApproximative IndexingHigh Dimensional IndexingDealing with Text DatabasesStatistical Supervised Machine LearningMultimodal FusionSoftware ArchitectureMultimedia Databases in Medicine Readership: Professionals, academics, researchers and graduate students in databases, artificial intelligence, pattern recognition and neural networks. Keywords:Big Data;Content-Based Multimedia Retrieval;Wavelets;Compression;Feature Extractopm;High-Dimensional Indexing;Information Retrieval;Machine Learning

Database Semantics: Semantic Issues in Multimedia Systems reflects the state of the art of emerging research on the meaning of multimedia information, as presented during IFIP's Eighth Data Semantics Working Conference (DS-8), organized by its Working Group 2.6 on Databases, and held at Rotorua, New Zealand, in January 1999. DS-8 was planned as an active forum for researchers and practitioners focusing on those issues that involve the semantics of the information represented, stored, and manipulated by multimedia systems. Depending on the topic and state of research, issues may be covered either deeply theoretically or quite practically, or even both. These proceedings contain twenty-one papers carefully selected by an International Programme Committee and organized in six thematic areas: Video Data Modelling and Use; Image Databases; Applications of Multimedia Systems; Multimedia Modeling in General; Multimedia Information Retrieval; Semantics and Metadata. For almost every area, important topics and issues include: data modeling and query languages for media such as audio, video, and images; methodological aspects of multimedia database design; intelligent multimedia information retrieval; knowledge discovery and data mining in multimedia information; multimedia user interfaces. Three visionary keynote addresses, by famous experts Ramesh Jain, Hermann Maurer and Masao Sakauchi, set the stage for discussion and future directions for the field. The collection of papers that resulted now offers a glimpse of the excitement and enthusiasm from DS-8. Database Semantics: Semantic Issues in Multimedia Systems is suitable as a secondary text for a graduate-level course on database systems, multimedia systems, or information retrieval systems and as a reference for practitioners and researchers in industry.

Business intelligence has always been considered an essential ingredient for success. However, it is not until recently that the technology has enabled organizations to generate and deploy intelligence for global competition. These technologies can be leveraged to create the intelligent enterprises of the 21st century that will not only provide excellent and customized services to their customers, but will also create business efficiency for building relationships with suppliers and other business partners on a long term basis. Creating such intelligent enterprises requires the understanding and integration of diverse enterprise components into cohesive intelligent systems. Anticipating that future enterprises need to become intelligent, Intelligent Enterprises of the 21st Century brings together the experiences and knowledge from many parts of the world to provide a compendium of high quality theoretical and applied concepts, methodologies, and techniques that help diffuse knowledge and skills required to create and manage intelligent enterprises of the 21st century for gaining sustainable competitive advantage in a global environment. This book is a comprehensive compilation of the state of the art vision and thought processes needed to design and manage globally competitive business organizations.

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.