Visual Data Mining of Multimedia Data for Social and Behavioral Studies

With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively dis...

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Vydáno v:Information visualization Ročník 8; číslo 1; s. 56 - 70
Hlavní autoři: Yu, Chen, Zhong, Yiwen, Smith, Thomas, Park, Ikhyun, Huang, Weixia
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.04.2009
SAGE PUBLICATIONS, INC
Témata:
Academic disciplines
Algorithms
Behavior
Cognitive-behavioral factors
Data
Data analysis
Data mining
Data quality
Hypotheses
Information retrieval
Laboratories
Multimedia
Multimedia computer applications
Researchers
Social factors
Sociology
Studies
Systems design
Time series
Visualization
pattern discovery
time series
visualization
multimedia
temporal data
data mining
ISSN:1473-8716, 1473-8724
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Shrnutí:With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.
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ISSN:1473-8716
1473-8724
DOI:10.1057/ivs.2008.32