Towards the architecture of an instructional multimedia database

The applicability of multimedia databases in education may be extended if they can serve multiple target groups, leading to affordable costs per unit for the user. In this contribution, an approach is described to build generic multimedia databases to serve that purpose. This approach is elaborated within the ODB Project ('Instructional Design of an Optical DataBase'); the term optical refers to the use of optical storage media to hold the audiovisual components. The project aims at developing a database in which a hypermedia encyclopedia is combined with instructional multimedia applications for different target groups at different educational levels. The architecture of the Optical Database will allow for switching between application types while working (for instance from tutorial instruction via the encyclopedia to a simulation and back). For instruction, the content of the database is thereby organized around so-called standard instruction routes: one route per target group. In the project, the teacher is regarded as the manager of instruction.\ud \ud From that perspective, the database is primarily organized as a teaching facility. Central to the research is the condition that the architecture of the Optical Database has to enable teachers to select and tailor instruction routes to their needs in a way that is perceived as logical and easy to use

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper