No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
SMP and Cluster Architectures for Retrieval of Images in Digital Libraries.
Abstract
This paper presents an overview over parallel architectures for the efficient realisation of digital libraries by considering image databases as an example. The state of the art approach for image retrieval uses a priori extracted features and limits the applicability of the retrieval techniques, as a detail search for objects and for other important elements can't be performed. Well-suited algorithms for dynamic feature extraction and comparison are not often applied, as they require huge computational and memory resources. Integration of parallel methods and architectures enables the use of these alternative approaches for improved classification and retrieval of documents in digital libraries. Therefore implemented prototypes on a symmetric multiprocessor (SMP) and on cluster architecture are introduced in the paper. Performance measurements with a wavelet-based template matching method resulted into a reasonable speedup.
ResearchGate has not been able to resolve any citations for this publication.
In this paper a system called CLIMS (CLausthal Image Management System) for content based image retrieval as an important subsystem of a general multimedia database is presented. It offers querying
by sketch and image example and uses colour and wavelet based features for the comparison of images. Each image in the database
is represented by a set of wavelet coefficients and colour attributes, which form the fundament for the retrieval.
In order to enable efficient similarity search two index structures, VP-Trees and Lq metric, are introduced and discussed. With the extension of the original VP-tree algorithm a ranking of the n most similar images is possible. The efficiency of the proposed retrieval methods is evaluated on a sample, general image
catalogue.
An abstract is not available.
Advances in technologies for scanning, networking, and CD-ROM, lower prices for large disk storage, and acceptance of common image compression and file formats have contributed to an increase in the number, size, and uses of on-line image collections. New tools are needed to help users create, manage, and retrieve images from these collections. We are developing QBIC (query by image content), a prototype system that allows a user to create and query image databases in which the image content -- the colors, textures, shapes, and layout of images and the objects they contain -- is used as the basis of queries. This paper describes two sets of algorithms in QBIC. The first are methods that allow `query by color drawing,' a form of query in which a user draws an approximate color version of an image, and similar images are retrieved. These are automatic algorithms in the sense that no user action is necessary during database population. Secondly, we describe algorithms for semi-automatic identification of image objects during database population, improving the speed and usability of this manually-intensive step. Once outlined, detailed queries on the content-properties of these individual objects can be made at query time.
During the last decade, all commercial database systems have included features for parallel processing into their products. This development has been driven by the fact that databases grow in size at considerable rates. According to the results of the 1998 `very large database contest' the world’s largest databases, which have reached a size of over 10TB, double in size every year. At that speed, they outgrow the increase in processor speed and memory size, so additional measures are required to accommodate the effects of rapidly growing volumes of data. Parallelism is one of those options. It helps to keep processing times constant, even if the size of the database increases. That effect, which is often referred to as `scaleup' is important for loading, index creation, all kinds of administrative operations on the database, and of course for long batch-type applications. Parallelism is also employed to speed-up queries that otherwise would take days or weeks to process and thus would be useless for the application. This type of requirement: fast results of complex queries on large data sets is characteristic of decision support applications. In this overview we will explain how parallelism in databases can help to solve such problems.
Over the last five or six years everybody has been talking about multimedia computing and how it would change our ways of doing business and of running our everyday lives. This discussion has been mostly based on technology becoming available, rather than a need of society itself to go ‘multimedia’. The continuing search for a so called ‘killer’-application is sufficient proof of this.
For the practical use of video retrieval systems, access must be
provided so as to bridge the semantic gap between the system and users.
Semiotics, which is concerned with the production of sense and of the
way in which it is received by humans appears as the formal background
for this goal. According to semiotics, semantics can be extracted at
different levels of signification through a suitable set of rules which
combine visual and auditory signs. An intermediate semantic level, which
has to do with the combination of low-level signs, such as color,
motion, and shape and their changes through time, accounts for the video
expressiveness and the emotions provoked. It can be obtained
automatically and is often useful for retrieval by content and
similarity-based classification of video streams. Applications to
retrieval of commercials based on expressive semantics are discussed