Wavelet-based feature extraction using probabilistic finite state automata for pattern classification

  • Authors:

  • Xin Jin;Shalabh Gupta;Kushal Mukherjee;Asok Ray

  • Affiliations:

  • Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA;Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA;Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA;Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2011

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Abstract

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.