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MDMM 2008 : 2nd International Workshop on Multimedia Data Mining and ManagementConference Series : Multimedia Data Mining and Management | |||||||||||||||
Link: http://www.bridgeport.edu/~jelee/mdmm | |||||||||||||||
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Call For Papers | |||||||||||||||
Workshop description and objective
With the recent advances in electronic imaging, video devices, storage, networking and computer power, the amount of multimedia has grown enormously, and data mining has become a popular way of discovering new knowledge from such a large data sets. Multimedia data mining is a discipline which brings together database systems, artificial intelligence, and multimedia processing, such as image and video processing. It is important to understand what is multimedia data mining, how data mining techniques can contribute to discover new knowledge, how to organize and manage the discovered knowledge and concepts. The multimedia data appear in multiple forms including audio, speech, text, web, image, video and combinations of several types. In this workshop, we aim to solicit papers that address the technical challenges in mining multimedia data and management. Through the workshop, we expect to bring together experts in analysis of multimedia data, state-of-art data mining and knowledge discovery in multimedia database systems, and domain experts in diverse areas, such as medical, surveillance, and education. Topics of contributions include (but are not limited to): Algorithms and Models Association rules for multimedia data mining Clustering algorithms for multimedia data mining Classification algorithms for multimedia data mining Conceptual clustering for multimedia data mining Neural networks for multimedia data mining Parallel and distributed data mining for multimedia data Multimedia data mining in pervasive computing Multimedia ontology Stream data mining algorithms Spatio-Temporal data mining and algorithms Applications Audio/Image/Video DBMSs Data mining system for medical multimedia data Multimedia segmentation Visualization Semantic web and annotation Summarization and abstraction Video abstraction Contents-based image/video retrieval systems |
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