Machine Learning Techniques for Adaptive Multimedia Retrieval: Technologies Applications and Perspectives disseminates current information on multimedia retrieval, advances the field of multimedia databases, and educates the multimedia database community. It is a critical text for professionals who are engaged in efforts to understand machine learning techniques for adaptive multimedia retrieval research, design and applications.

In recent years, libraries have embraced new technologies that organize and store a variety of digital information, such as multimedia databases, digital medical images, and content-based images. Modern Library Technologies for Data Storage, Retrieval, and Use highlights new features of digital library technology in order to educate the database community. By contributing research from case studies on the emerging technology use in libraries, this book is essential for academics and scientists interested in the efforts to understand the applications of data acquisition, retrieval and storage.

Digital libraries are libraries in which collections are stored in digital formats (as opposed to print, microform, or other media) and accessible by computers. Multimedia Storage and Retrieval Innovations for Digital Library Systems offers the latest research on retrieval and storage methods for digital library systems, a burgeoning field of data sourcing. In order to stay abreast of the latest terminology, case studies, frameworks, architectures, methodologies, and research within the field of digital libraries, it is crucial to keep up with the latest literature. The information in this compendium comes from contributing authors and experts around the world, making it a one of a kind addition to any research library.