吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 822-827.doi: 10.13229/j.cnki.jdxbgxb201403039

• Orignal Article • Previous Articles     Next Articles

Special object recognition based on sparse representation in multiclass fusion sample

ZHA Chang-jun1,2,WEI Sui1,YANG Hai-rong3,DING Da-wei1   

  1. 1.Key Laboratory of Intelligent Computing and 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.Department of Mathematics, Hefei Normal University,Hefei 230061,China
  • Received:2013-02-26 Online:2014-03-01 Published:2014-03-01

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Abstract: According to the characteristics of Wireless Sensor Network (WSN) and the profile detecting system, a data processing method based on WSN is proposed. In this method, first, the sample features are extracted by principal component analysis; then, the features of different samples were fused using accumulate mode. A mathematical model was given and on the basis of this model, a novel algorithm of special object recognition based on sparse representation in multiclass fusion sample was proposed. This algorithm recognizes the special target according to distribution of the main non-zero coefficients under an over-complete dictionary. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm, the comprehensive performance is better than the traditional methods.

Key words: information processing, wireless sensor network, sparse representation, profiling recognition, unattended ground sensor

CLC Number: 

  • TN911.74
[1] Russomanno David J, Chari Srikan, Emmanuel Kenny, et al. Testing and evaluation of profiling sensors for perimeter security[J]. ITEA, 2010,31(1):121-130.
[2] Sartain Ronald B, Aliberti Keith, Alexander Troy, et al. Long-wave infrared profile feature extractor (PFx) sensor[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2009.
[3] Russomanno David J, Chari Srikant, Jacobs Eddie L,et al. Near-IR sparse detector sensor for intelligent electronic fence applications[J]. IEEE Sensors Journal, 2010, 10(6):1106-1107.
[4] Chari Srikant, Halford Carl, Jacobs Eddie,et al. Classification of humans and animals using an infrared profiling sensor[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2009.
[5] Russomanno David, Chari Srikan, Halford Carl. Sparse detector imaging sensor with two-class silhouette classification[J]. Sensors, 2008(8):7996-8015.
[6] Reynolds R K, Chari S, Russomanno D J. Embedded real-time classifier for profiling sensors and custom detector configuration[C]∥Proc of SPIE, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR II,2011.
[7] 查长军,孙南,张成,等. 基于稀疏表示的特定目标识别[J].吉林大学学报:工学版,2013,43(1):256-260.
Zha Chang-jun, Sun Nan, Zhang Cheng,et al. Special object recognition based on sparse representation[J].Journal of Jilin University (Engineering and Technology Edition), 2013,43(1):256-260.
[8] Chen S, Donoho D, Saunders M. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1):129-159.
[9] Li Yu-long, Feng Ju-fu. Sparse represention shape model[C]∥Proceedings of 2010 IEEE 17th ICIP, 2010:2733-2736.
[10] Wright John, Yang Allen Y, Ganesh Arvind, et al. Robust face recognition via sparse representation[J]. IEEE Transactions, PAMI, 2009, 31(2):1-18.
[11] Yang Jian,Zhang David,Frangi Alejandro F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26(1):131-137.
[12] Russomanno D, Yeasin M, Jacobs E, et al. Sparse detector sensor: profiling experiments for broad-scale classification[C]∥Proc of SPIE, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI,Orlando, FL,2008.
[13] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006,52(1):6-18.
[14] Rubinstein R, Bruckstein A M, Elad M. Dictionaries for sparse representation modeling[J]. Proceedings of the IEEE, 2010, 98(6): 1045-1057.
[15] Smith Leslie N, Elad Michael. Improving dictionary learning: multiple dictionary updates and coefficient reuse[J]. IEEE Signal Processing Letters, 2013, 20(1):79-82.
[16] Hastie Trevor, Tibshirani Robert, Friedman Jerome. The Elements of Statistical Learning[M]. New York:Springer-Verlag, 2001:14-18.
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