Mining Multimedia and Complex Data: KDD Workshop MDM/KDD 2002, PAKDD Workshop KDMCD 2002, Revised Papers / Edition 1

by Osmar R. Zaiane, Simeon Simoff, Chabane Djeraba

Overview

1 WorkshopTheme Digital multimedia difiers 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 terms of underlying models and formats is that it is synchronized and integrated, 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 profiles, the content generated by the virtual community, and communication data in different 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.

Product Details

ISBN-13:	9783540203056
Publisher:	Springer Berlin Heidelberg
Publication date:	12/05/2003
Series:	Lecture Notes in Computer Science , #2797
Edition description:	2003
Pages:	284
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

Subjective Interpretation of Complex Data: Requirements for Supporting Kansei Mining Process.- Multimedia Data Mining Framework for Raw Video Sequences.- Object Detection for Hierarchical Image Classification.- Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrieval.- Associative Classifiers for Medical Images.- An Innovative Concept for Image Information Mining.- Multimedia Data Mining Using P-Trees.- Scale Space Exploration for Mining Image Information Content.- Videoviews: A Content Based Video Description Schema and Database Navigation Tool.- The Community of Multimedia Agents.- Multimedia Mining of Collaborative Virtual Workspaces: An Integrative Framework for Extracting and Integrating Collaborative Process Knowledge.- STIFF: A Forecasting Framework for SpatioTemporal Data.- Mining Propositional Knowledge Bases to Discover Multi-level Rules.- Meta-classification: Combining Multimodal Classifiers.- Partition Cardinality Estimation in Image Repositories.- A Framework for Customizable Sports Video Management and Retrieval.- Style Recognition Using Keyword Analysis.