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Wavelet-based feature extraction using probabilistic finite state automata for pattern classification
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DOI：10.1016/j.patcog.2010.12.003.png)
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Real-time data-driven pattern classification requires extraction of relevant features from the observed time series as low-dimensional and yet information-rich representations of the underlying dynamics. These low-dimensional features facilitate in situ decision-making in diverse applications, such as computer vision, structural health monitoring, and robotics. Wavelet transforms of time series have been widely used for feature extraction owing to their time-frequency localization properties. In this regard, this paper presents a symbolic dynamics-based method to model surface images, generated by wavelet coefficients in the scale-shift space. These symbolic dynamics-based models (e.g., probabilistic finite state automata (PFSA)) capture the relevant information, embedded in the sensor data, from the associated Perron-Frobenius operators (i.e., the state-transition probability matrices). The proposed method of pattern classification has been experimentally validated on laboratory apparatuses for two different applications: (i) early detection of evolving damage in polycrystalline alloy structures, and (ii) classification of mobile robots and their motion profiles. (c) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Time series analysis
Symbolic dynamics
Feature extraction
Pattern classification
Probabilistic finite state automata
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7.6
论文数:
1.3W
被引数:
4.5W
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