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Multimedia database

About: Multimedia database is a research topic. Over the lifetime, 1404 publications have been published within this topic receiving 19856 citations. The topic is also known as: Multimedia database & MMDB.


Papers
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Journal ArticleDOI
01 Feb 1991
TL;DR: The definition of multilinguality is given and a framework for a multilingual database of learning materials is presented, and a general specification of multilingual aspects of a browser system is presented.
Abstract: This article presents a summary of approaches to the development of multilingual software and relevant multicultural issues. It gives a definition of multilinguality and moves on to present a framework for a multilingual database of learning materials. In this context, a general specification of multilingual aspects of a browser system are presented. In addition, the article considers the multicultural aspects of a database of learning materials. A number of problems are identified which need to be dealt with in this context.

4 citations

Journal ArticleDOI
TL;DR: An extensive field test of the MILES system has been applied as a common source for all kinds of materials used in teaching physiology to students of medicine, showing that the "new media" are powerful instruments for improving teaching and learning.
Abstract: A teaching-oriented multimedia database authoring system, MILES ( Multimediales Informations- und Lehrsystem), has been in development since 1980 in our department. The hardware consists of a network of personal computers connected to digital and, until recently, audio/video storage devices. The system provides a database capable of handling all kinds of multimedia data and computer programs. User-friendly software provides input, editing, retrieval, and communication; the authoring system allows these components to be organized into structures of complex menus, combined with free database access. More than 12,000 components have been stored, including approximately 3,500 pictures. The paper reports on an extensive field test, in which the system has been applied as a common source for all kinds of materials used in teaching physiology to students of medicine. ResuIts show that the "new media" are powerful instruments for improving teaching and learning. However, they should not be expected to provide the sole basis for education. Their application still faces many problems regarding concepts, efficiency, and acceptance by students and staff.

4 citations

01 Apr 1991
TL;DR: It is argued that captions can be naturally expressed in a restricted language whose interpretations is easier than general natural- language understanding and used in automated use of captions in retrieval from computerized multimedia databases.
Abstract: : Descriptive captions help organize noncompetitive media. But automated use of captions in retrieval from computerized multimedia databases has not been much examined because it would seem to require significant natural language processing. We argue that captions can be naturally expressed in a restricted language whose interpretations is easier than general natural- language understanding. We describe a multimedia database system that stores interpreted captions in predicate calculus for each media datum; it then interprets restricted-language queries, and finds matching media objects.

4 citations

15 Sep 2007
TL;DR: A search system in which a request might be an image file or a keyword and an algorithm to calculate a similarity distance between two XML nodes with a given precision 'k' is proposed so as to be able to provide accurate information in response to a user request.
Abstract: This paper presents a formalization of an image retrieval system based on a notion of similarity between images in a multimedia database (namely XML-Enabled Database) and where a user request can be an image file or a keyword. The CBIR (Content Based Image Retrieval) system and the current search engines (e.g. Google, Yahoo....) make image search possible only when the query is a keyword. This type of search is limited because keywords are not expressive enough to describe all important characteristics of an image. For example, an exact match request cannot be formulated in such systems. Thus, we propose a search system in which a request might be an image file or a keyword. The MPEG-7 standard is used for describing an image as an XML document. A similarity distance between images is defined which is used to compare the request image with the images of a database. We also propose an algorithm to calculate a similarity distance between two XML nodes with a given precision 'k' (k is defined by the user: he can fix 'k' at 100% for the exact match retrieval of features) so as to be able to provide accurate information in response to a user request. The statistics show that our system is more efficient than leading content based image retrieval systems such as ERIC7 and current search engines.

4 citations

Journal ArticleDOI
TL;DR: The proposed model uses novel text and image features that allow it to differentiate between geometrical images (GIs) and ordinary images and is able to categorize correctly images with an expected increase in similarity matching as larger datasets and neural document classifier (NDC) are used.
Abstract: Automatic image categorization and description are key components for many applications, i.e., multimedia database management, web content analysis, human–computer interactions, and biometrics. In general, image description is a difficult task because of the wide variety of objects potentially to be recognized and the complexity and variety of backgrounds. This paper introduces a computational model for context-based image categorization and description. First, for a given image, a classifier is trained by the associated text features using advanced concepts, so that it can assign the image to a specific category. Then, a similarity matching with that category's annotated templates is performed for images in every other category. The proposed model uses novel text and image features that allow it to differentiate between geometrical images (GIs) and ordinary images. The experimental results show that the model is able to categorize correctly images with an expected increase in similarity matching as larger datasets and neural document classifier (NDC) are used. An important feature of the proposed model is that its specific matching techniques, suitable for a particular category, can be easily integrated and developed for other categories.

4 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232
20224
202113
20206
201911
201824