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Mining Multimedia and Complex Data : KDD Workshop MDM/KDD 2002, PAKDD Workshop KDMCD 2002, Revised Papers - Osmar R. Zaiane

Mining Multimedia and Complex Data

KDD Workshop MDM/KDD 2002, PAKDD Workshop KDMCD 2002, Revised Papers

By: Osmar R. Zaiane (Editor), Simon Simoff (Editor), Chabane Djeraba (Editor)

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Published: 13th October 2003
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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.

Subjective Interpretation of Complex Data: Requirements for Supporting Kansei Mining Processp. 1
Multimedia Data Mining Framework for Raw Video Sequencesp. 18
Object Detection for Hierarchical Image Classificationp. 36
Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrievalp. 50
Associative Classifiers for Medical Imagesp. 68
An Innovative Concept for Image Information Miningp. 84
Multimedia Data Mining Using P-Treesp. 100
Scale Space Exploration for Mining Image Information Contentp. 118
Videoviews: A Content Based Video Description Schema and Database Navigation Toolp. 134
The Community of Multimedia Agentsp. 149
Multimedia Mining of Collaborative Virtual Workspaces: An Integrative Framework for Extracting and Integrating Collaborative Process Knowledgep. 164
STIFF: A Forecasting Framework for Spatio-Temporal Datap. 183
Mining Propositional Knowledge Bases to Discover Multi-level Rulesp. 199
Meta-classification: Combining Multimodal Classifiersp. 217
Partition Cardinality Estimation in Image Repositoriesp. 232
A Framework for Customizable Sports Video Management and Retrievalp. 248
Style Recognition Using Keyword Analysisp. 266
Author Indexp. 281
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9783540203056
ISBN-10: 3540203052
Series: Lecture Notes in Computer Science
Audience: General
Format: Paperback
Language: English
Number Of Pages: 284
Published: 13th October 2003
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.5 x 15.5  x 1.6
Weight (kg): 0.94