Mining Multimedia and Complex Data: KDD Workshop MDM/KDD 2002. PAKDD Workshop KDMCD 2002. Revised Papers

Nadia Bianchi-Berthouze, Tomofumi Hayashi (auth.), Osmar R. Zaïane, Simeon J. Simoff, Chabane Djeraba (eds.)3540203052, 9783540203056

1 WorkshopTheme Digital multimedia di?ers from previous forms of combined media in that the bits that represent text, images, animations, and audio, video and other signals can be treated as data by computer programs. One facet of this diverse data in termsofunderlyingmodelsandformatsisthatitissynchronizedandintegrated, hence it can be treated as integral data records. Such records can be found in a number of areas of human endeavour. Modern medicine generates huge amounts of such digital data. Another – ample is architectural design and the related architecture, engineering and c- struction (AEC) industry. Virtual communities (in the broad sense of this word, which includes any communities mediated by digital technologies) are another example where generated data constitutes an integral data record. Such data may include data about member pro?les, the content generated by the virtual community, and communication data in di?erent formats, including e-mail, chat records, SMS messages, videoconferencing records. Not all multimedia data is so diverse. An example of less diverse data, but data that is larger in terms of the collected amount, is that generated by video surveillance systems, where each integral data record roughly consists of a set of time-stamped images – the video frames. In any case, the collection of such in- gral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has led to the research and devel- ment in the area of multimedia data mining.

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Table of contents :
Front Matter….Pages –
Subjective Interpretation of Complex Data: Requirements for Supporting Kansei Mining Process….Pages 1-17
Multimedia Data Mining Framework for Raw Video Sequences….Pages 18-35
Object Detection for Hierarchical Image Classification….Pages 36-49
Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrieval….Pages 50-67
Associative Classifiers for Medical Images….Pages 68-83
An Innovative Concept for Image Information Mining….Pages 84-99
Multimedia Data Mining Using P-Trees….Pages 100-117
Scale Space Exploration for Mining Image Information Content….Pages 118-133
Videoviews: A Content Based Video Description Schema and Database Navigation Tool….Pages 134-148
The Community of Multimedia Agents….Pages 149-163
Multimedia Mining of Collaborative Virtual Workspaces: An Integrative Framework for Extracting and Integrating Collaborative Process Knowledge….Pages 164-182
STIFF: A Forecasting Framework for SpatioTemporal Data….Pages 183-198
Mining Propositional Knowledge Bases to Discover Multi-level Rules….Pages 199-216
Meta-classification: Combining Multimodal Classifiers….Pages 217-231
Partition Cardinality Estimation in Image Repositories….Pages 232-247
A Framework for Customizable Sports Video Management and Retrieval….Pages 248-265
Style Recognition Using Keyword Analysis….Pages 266-280
Back Matter….Pages –