Biomimetic (topological) pattern recognition - a new model of pattern recognition theory and its application

Wang, Shoujue; Chen, Xu

doi:10.1109/ijcnn.2003.1223762

Cited by 22 publications

(19 citation statements)

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“…From the aspect of understanding the world by human beings, Wang Shoujue, a member of the Chinese Academy of Sciences, proposed the theory of "Biomimetic Pattern Recognition" (BPR) [9][10][11] , which states that people recognize things one by one by finding the commonalities between things in the same class. Thus, assuming that in the feature space, the sample points of the same class would have continuous, recognizable characters.…”

Section: A Biomimetic Pattern Recognitionmentioning

confidence: 99%

“…This principle of continuity among homologous samples in feature space is called the Principle of Homology-Continuity (PHC). The mathematical description of PHC [9,11] is that in feature space, we suppose the set A is a point set including all samples in class A. Hence, if x, y ∈ A and 0 ε > are given, there must exist a set…”

Section: A Biomimetic Pattern Recognitionmentioning

confidence: 99%

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Recognition of Plant mRNA Polyadenylation Sites Based on High-Dimensional Space Points' Covering Method

Zheng

Zou

et al. 2008

2008 International Symposium on Information Science and Engineering

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To accurately predict messenger RNA polyadenylation sites of the model plant Arabidopsis, we developed a new program using the method of HighDimensional Space Points' Covering, based on Biomimetic Pattern Recognition Theory. Upon analysis of experimental data and comparing with the results produced by Support Vector Machine, it is demonstrated that our program is reliable and effective judged by its specificity and sensitivity of the prediction. The program performed especially well with a reduced training sample size.

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Section: A Biomimetic Pattern Recognitionmentioning

confidence: 99%

Section: A Biomimetic Pattern Recognitionmentioning

confidence: 99%

Recognition of Plant mRNA Polyadenylation Sites Based on High-Dimensional Space Points' Covering Method

Zheng

Zou

et al. 2008

2008 International Symposium on Information Science and Engineering

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“…Because there are more fault patterns and less fault samples in the sucker-pod pump working conditions, the above approaches have their limits, respectively. In recent years, a method called biomimetic pattern recognition (BPR) [12] has been proposed, which is based on the principle of homology continuity and is suitable to recognize and classify those objects with more pattern types and less samples.…”

Section: Introductionmentioning

confidence: 99%

Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

Shi

2013

Journal of Control Science and Engineering

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Three feature extraction methods of sucker-rod pump indicator card data have been studied, simulated, and compared in this paper, which are based on Fourier Descriptors (FD), Geometric Moment Vector (GMV), and Gray Level Matrix Statistics (GLMX), respectively. Numerical experiments show that the Fourier Descriptors algorithm requires less running time and less memory space with possible loss of information due to nonoptimal numbers of Fourier Descriptors, the Geometric Moment Vector algorithm is more time-consuming and requires more memory space, while the Gray Level Matrix Statistics algorithm provides low-dimension feature vectors with more time consumption and more memory space. Furthermore, the characteristic of rotational invariance, both in the Fourier Descriptors algorithm and the Geometric Moment Vector algorithm, may result in improper pattern recognition of indicator card data when used for sucker-rod pump working condition diagnosis.

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“…This can avoid the direct calculation on the amplitude information subjected to noise by quantifying the phase information in a local image window. Moreover, Biomimetic Pattern Recognition (BPR) [5] was first introduced to the field of SAR image recognition by this paper though it has been widely used in image recognition field and has achieved outstanding performances. According BPR theory, we can consider that the variation of orientations of the same type targets is continuous, and the high dimensional geometry space covering can be constructed by hyper sausage neurons through best coverage of feature samples.…”

Section: Introductionmentioning

confidence: 99%

SAR automatic target recognition based on Local Phase Quantization plus Biomimetic Pattern Recognition

Zhai

Guo

et al. 2012

2012 IEEE 11th International Conference on Signal Processing

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Due to the needs of the modern battlefield awareness and military reconnaissance, Synthetic Aperture Radar (SAR) automatic target recognition (ATR) has been receiving more and more attention. In this paper, a SAR ATR algorithm based on Local Phase Quantization (LPQ) plus Biomimetic Pattern Recognition (BPR) has been proposed. There are three main steps in the proposed algorithm: firstly, a simple preprocessing procedure based on centroid location is applied to the original SAR image to extract the target's region of interest. Secondly, short-term Fourier transform (STFT) computed over the subwindows at every pixel of the preprocessed image is performed and the information of the Fourier phase spectrum is quantized as the features to overcome the target azimuth angle variation. Finally, high dimensional geometry space covering method based on BPR theory is adopted and the hyper-sausage neuron is employed to construct the hyperlinks for the purpose of recognition. Experiments on the MSTAR database show that this proposed algorithm can achieve relatively high recognition rate and high rejection rate.

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Biomimetic (topological) pattern recognition - a new model of pattern recognition theory and its application

Cited by 22 publications

References 1 publication

Recognition of Plant mRNA Polyadenylation Sites Based on High-Dimensional Space Points' Covering Method

Recognition of Plant mRNA Polyadenylation Sites Based on High-Dimensional Space Points' Covering Method

Research on Feature Extraction of Indicator Card Data for Sucker-Rod Pump Working Condition Diagnosis

SAR automatic target recognition based on Local Phase Quantization plus Biomimetic Pattern Recognition

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