吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (01): 256-260.

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Special object recognition based on sparse representation

ZHA Chang-jun1,2, SUN Nan3, ZHANG Cheng1, WEI Sui1   

  1. 1. Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China;
    2. Key Laboratory of Machine Vision and Intelligence Control Technology, Hefei University, Hefei 230601, China;
    3. Troops 73101 of PLA, Xuzhou 221008, China
  • Received:2012-06-04 Online:2013-01-01 Published:2013-01-01

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Abstract: According to the output signal characteristics of the profile detecting system, special object recognition method based on sparse representation, combined with the theory of sparse representation and principal component analysis, is proposed. First, using principal component analysis, the method extracts the main components of the sample signal in order to eliminate redundant information. Second, the signal is transformed into the same size of the feature vectors, which is then projected to the lower dimensional space to construct a dictionary. Finally, the testing samples are sparsely represented and recognized by the dictionary. Numerical simulations and experiments show that the proposed method has good classification effect in lower dimensional space, and good robustness for the system with some damage sensors in the actual situation.

Key words: information processing, sparse representation, profiling recognition, feature extraction

CLC Number: 

  • TN911.74
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