The phenomenal growth in multimedia content has lead to the development of a variety of multimedia description schemes, which can be used to facilitate querying of multimedia databases. In the increasingly mobile envi...
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The phenomenal growth in multimedia content has lead to the development of a variety of multimedia description schemes, which can be used to facilitate querying of multimedia databases. In the increasingly mobile environment of today, multimedia query formats need to be applicable to mobile devices, which, compared to desktop PCs, have specific limitations such as small screen size, limited memory and processing power and high bandwidth cost. As a potential solution to multimedia querying in mobile environments, this paper introduces two concepts: query streaming and its application as targeted browsing. Targeted browsing is a technique for multimedia query-by-content designed especially for mobile devices while query streaming is a method for continually updating a query by sending additional terms to an existing query. This paper describes an implementation of query streaming that combines the Multimedia query Format (MQF) (a standard communication language for querying multimedia databases) with Fragment Request Units (FRU) and Fragment Update Units (FUU) (which provide a standard way of randomly accessing fragments of XML documents). For efficient compression of the multimedia query XML files, the use of binary compression using MPEG BiM is proposed and a number of use case scenarios are examined. Results show that the proposed solution to provide a significant reduction in the file size required to perform multimedia querying. (C) 2008 Elsevier Ltd. All rights reserved.
Nowadays we are experiencing a remarkable growth in the number of databases that have become accessible over the Web. However, in a certain number of cases, for example, in the case of BioImage, this information is no...
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Nowadays we are experiencing a remarkable growth in the number of databases that have become accessible over the Web. However, in a certain number of cases, for example, in the case of BioImage, this information is not of a textual nature, thus posing new challenges in the design of tools to handle these data. In this work, are concentrate on the development of new mechanisms aimed at "querying" these databases of complex data sets by their intrinsic content, rather than by their textual annotations only. We concentrate our efforts on a subset of BioImage containing 3D images (volumes) of biological macromolecules, implementing a first prototype of a "query-by-content" system. In the context of databases of complex data types the term query-by-content makes reference to those data modeling techniques in which user-defined functions aim at "understanding" (to some extent) the informational content of the data sets. In these systems the matching criteria introduced by the user are related to intrinsic features concerning the 3D images themselves, hence, complementing traditional queries by textual key words only. Efficient computational algorithms are required in order to "extract" structural information of the 3D images prior to storing them in the database. Also, easy-to-use interfaces should be implemented in order to obtain feedback from the expert. Our query-by-content prototype is used to construct a concrete query, making use of basic structural features, which are then evaluated over a set of three-dimensional images of biological macromolecules. This experimental implementation can be accessed via the Web at the BioImage server in Madrid, at http://***/qbc/***. (C) 1999 Academic Press.
We describe a query-by-content search engine that enables a radiologist to search a large database of diagnostically-proven ("benign" or "malignant") mammographic regions of interest (ROIs). The da...
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ISBN:
(纸本)0819440094
We describe a query-by-content search engine that enables a radiologist to search a large database of diagnostically-proven ("benign" or "malignant") mammographic regions of interest (ROIs). The database search is facilitated by a relational map which is a two-dimensional display of all the ROIs in the database. Labeled points on the map represent ROIs in the database. The map is constructed from the output of a neural network that has been trained to cluster the ROIs in the database using a measure of perceptual similarity. To use the search facility of our computer-aided diagnosis system a radiologist selects a ROI from a digitized mammogram and submits the ROI as a query to the search engine. The search engine first maps the query ROI to its appropriate location on the relational map. The search engine then retrieves the ROIs that are closest to the query ROI on the relational map. These retrieved ROIs are from the same cluster on the relational map. The results of the search are presented to the radiologist in the form of the retrieved ROIs, along with related information such as biopsy result and patient age. The radiologist can also perform an unrestricted search by selecting any point on the relational map. The search engine will then return the closest ROIs to the selected point. The search engine is implemented using a three-layer distributed architecture. The first layer is a Java-based user interface that allows a radiologist to view a digital mammogram, to enhance the mammogram, to select a ROI, and to query the database. The second layer is a web server that generates HTML for the web client and provides access to the image processing algorithms, the neural network, and the image search functions. The third layer is a remote database containing the ROIs and associated patient information. The embedding of this search engine into an integrated diagnostic system may help the radiologist to incorporate subtle image relationships into the diagnostic process
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