scholarly journals Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

2021 ◽  
Vol 38 (6) ◽  
pp. 1599-1611
Author(s):  
Hong Yang ◽  
Yanming Zhao ◽  
Guoan Su ◽  
Xiuyun Liu ◽  
Songwen Jin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Basis Function ◽  
Recognition Rate ◽  
Basis Functions ◽  
Visual Selection ◽  
Natural Images ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Invariance Theory ◽  
Selection Consistency

The conventional slow feature analysis (SFA) algorithm has no support of computational theory of vision for primates, nor does it have the ability to learn the global features with visual selection consistency continuity. And what is more, the algorithm is highly complex. Based on this, Slow Feature Extraction Algorithm Based on Visual selection consistency continuity and Its Application was proposed. Inspired by the visual selection consistency continuity theory for primates, this paper replaced the principal component analysis (PCA) method of the conventional SFA algorithm with the myTICA method, extracted the Gabor basis functions of natural images, initialized the basis function family; it used the feature basis expansion algorithm based on visual selection consistency continuity (the VSCC_FBEA algorithm) to replace the polynomial expansion method in the original SFA algorithm to generates the Gabor basis functions of features with long and short-term visual selectivity in the family of basis functions, which solved the drawbacks of the polynomial prediction algorithm; it also designed the Lipschitz consistency constraint, and proposed the Lipschitz-Orthogonal-Pruning-Method (LOPM algorithm) to optimize the basis function family into an over-complete family of basis functions. In addition, this paper used the feature expression method based on visual invariance theory (visual invariance theory -FEM) to establish the set of features of natural images with visual selection consistency continuity. Subsequently, it adopted three error evaluation methods and mySFA classification method to evaluate the proposed algorithm. According to the experimental results, the proposed algorithm showed good prediction performance with respect to the LSVRC2012 data set; compared with the SFA, GSFA, TICA, myICA and mySFA algorithms, the proposed algorithm is correct and feasible; when the classification threshold of the algorithm was set at 8.0, the recognition rate of the proposed algorithm reached 99.66%, and neither of the false recognition rate and the false rejection rate was higher than 0.33%. The proposed algorithm has good performance in prediction and classification, and also shows good anti-noise capacity under limited noise conditions.

scholarly journals A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingchao Li ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Cloud Model ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Communication Signals ◽  
Low Snr ◽  
Feature Extraction Algorithm ◽  
Cloud Characteristics ◽  
Extraction Algorithm ◽  
New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

scholarly journals Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shan Guan ◽  
Kai Zhao ◽  
Shuning Yang
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
Motor Imagery ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Classification Algorithm ◽  
Classification Error ◽  
K Nearest Neighbor ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.

scholarly journals FRACTAL COMPLEXITY-BASED FEATURE EXTRACTION ALGORITHM OF COMMUNICATION SIGNALS

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740008 ◽  
Author(s):  
HUI WANG ◽  
JINGCHAO LI ◽  
LILI GUO ◽  
ZHENG DOU ◽  
YUN LIN ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Radiation Source ◽  
Recognition Rate ◽  
Individual Characteristics ◽  
Accurate Identification ◽  
Communication Signals ◽  
Low Snr ◽  
Communication Signal ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of −10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

An Improved Algorithm for Gesture Recognition

2013 ◽  
Vol 756-759 ◽  
pp. 3914-3919
Author(s):  
Ping Ping Chen ◽  
Ding Ying Tan ◽  
Qian Qian Xu ◽  
Qing Zhong Liang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Gesture Recognition ◽  
Experimental Test ◽  
Recognition Rate ◽  
Complex Environment ◽  
Time Performance ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Key Steps

In this paper, gesture recognition is being research which focuses on the key steps - gesture feature extraction. Also, designed and implemented a gesture feature extraction algorithm in a complex environment. The experimental test proved that the feature extraction algorithm has better real - time performance and higher recognition rate, which achieve the desired objectives.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Visible Images
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