scholarly journals Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianmin Ma ◽  
Xiaofeng Li
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Extraction Method ◽  
Feature Recognition ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Residual Network ◽  
Feature Extraction Method ◽  
Contour Feature ◽  
Dynamic Gestures

The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

Research of Feature Extraction Method on Facial Expression Change

2011 ◽  
Vol 211-212 ◽  
pp. 813-817 ◽  
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Facial Expression ◽  
Extraction Method ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Feature Extraction Method ◽  
Low Dimensional

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

Kernel Orthogonal Neighborhood Preserving Discriminant Analysis

2011 ◽  
Vol 339 ◽  
pp. 571-574
Author(s):  
Xing Zhu Liang ◽  
Jing Zhao Li ◽  
Yu E Lin
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Extraction Method ◽  
Learning Algorithms ◽  
Recognition Rate ◽  
Kernel Matrix ◽  
Feature Extraction Method ◽  
Nonlinear Features ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kernel Orthogonal Neighborhood Preserving Discriminant Analysis (KONPDA). A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample. Then running KONPDA in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KONPDA method achieves higher recognition rate than the ONPDA method and other kernel-based learning algorithms.

scholarly journals A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 624 ◽  
Author(s):  
Zhe Chen ◽  
Yaan Li ◽  
Renjie Cao ◽  
Wasiq Ali ◽  
Jing Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Noise Reduction ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Normalized Mutual Information ◽  
Radiated Noise ◽  
New Feature

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

2015 ◽  
Vol 40 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Sayf A. Majeed ◽  
Hafizah Husain ◽  
Salina A. Samad
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Speech Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Extraction Method ◽  
Female Data ◽  
Recognition Systems ◽  
New Feature

Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 468 ◽  
Author(s):  
Dongri Xie ◽  
Hamada Esmaiel ◽  
Haixin Sun ◽  
Jie Qi ◽  
Zeyad A. H. Qasem
Keyword(s):  
Feature Extraction ◽  
Correlation Coefficient ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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