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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.
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