Time series analysis and segmentation using eigenvectors for mining semantic audio label sequences

Pattern discovery from video has promising applications in summarizing different genre types, including surveillance and sports. After pattern discovery, a summary of the video can be constructed from a combination of usual and unusual patterns, depending on the application domain. Previously, we us...

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Bibliographic Details
Main Authors: Radhakrishnan, R., Ziyou Xiong, Divakaran, A., Kan, T.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Broadcasting
Computer science
control theory
systems
Event detection
Exact sciences and technology
Face detection
Feedback
Multimedia communication
Pattern recognition. Digital image processing. Computational geometry
Speech
Supervised learning
Surveillance
Testing
Time series analysis
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Summary:Pattern discovery from video has promising applications in summarizing different genre types, including surveillance and sports. After pattern discovery, a summary of the video can be constructed from a combination of usual and unusual patterns, depending on the application domain. Previously, we used an unsupervised label mining approach to extract highlight moments from soccer videos (Radhakrishan, R. et al., IEEE Pacific-Rim Conf. on Multimedia, 2003). We now formulate the problem of pattern discovery from semantic audio labels as a time series clustering problem and propose a new unsupervised mining framework based on segmentation theory using eigenvectors of the affinity matrix. We test the validity of the technique using synthetically generated label sequences as well as label sequences from broadcast sports video. Our sports highlights extraction accuracy is comparable to that achieved in our previous work
DOI:10.1109/ICME.2004.1394266