Information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Optimizing recall/precision scores in IR over the WWW
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A stochastic framework for optimal key frame extraction from MPEG video databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW
Autonomous Agents and Multi-Agent Systems
Term Weighting Approaches in Automatic Text Retrieval
Term Weighting Approaches in Automatic Text Retrieval
SIFT: a tool for wide-area information dissemination
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Usage derived recommendations for a video digital library
Journal of Network and Computer Applications
Live television in a digital library
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Current multimedia databases contain a wealth of information in the form of audiovisual, as well as text data. Even though efficient search algorithms have been developed for either media, there still exists the need for abstract presentation and summarization of the results of database users' queries. Moreover, multimedia retrieval systems should be capable of providing the user with additional information related to the specific subject of the query, as well as suggest other topics which users with a similar profile are interested in. In this paper, we present a number of solutions to these issues, giving as an example an integrated architecture we have developed, along with notions that support efficient and secure Internet access and easy addition of new material. Segmentation of the video in shots is followed by shot classification in a number of predetermined categories. Generation of users' profiles according to the same categories, enhanced by relevance feedback, permits an efficient presentation of the retrieved video shots or characteristic frames in terms of the user interest in them. Moreover, this clustering scheme assists the notion of “lateral” links that enable the user to continue retrieval with data of similar nature or content to those already returned. Furthermore, user groups are formed and modeled by registering actual preferences and practices; this enables the system to “predict” information that is possibly relevant to specific users and present it along with the returned results. The concepts utilized in this system can be smoothly integrated in MPEG-7 compatible multimedia database systems.