Support Vector Machines For Recognition Research Articles

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

Determination of microplastics by FTIR spectroscopy based on quaternion parallel feature fusion and support vector machine

This study proposed a FTIR spectroscopy combing with quaternion parallel feature extraction methodology for the first time to address the qualitative and quantitative analysis of microplastics. For the international available microplastic FTIR dataset, the original, the first derivative and the second derivative FTIR spectra of polyethylene(PE), PE + fouling and polypropylene(PP) samples were first formed as an integral representation by a pure quaternion matrix. Then quaternion principal component analysis(QPCA) was conducted to obtain quaternion feature vectors and support vector machine(SVM) was followed to establish the qualitative model. Meanwhile, we experimentally mixed four types of microplastics of polyvinyl chloride(PVC), polystyrene (PS), PP and PE into sand to prepare simulated samples and collected their FTIR spectra. The concentrations of microplastics were 0.5%, 1%, 1.5%, 3%, 5%, 10%, respectively. The QPCA based parallel feature extraction combing with SVR was conducted to establish the quantitative model. The results show that QPCA has transcended traditional PCA and serial feature extraction methods. Furthermore, compared with traditional algorithms of PLS, BPNN, RF, and 1D-CNN, QPCA-SVM has achieved the highest values of accuracy, precision, recall and F1-score, which were 0.98571, 1, 0.99412 and 0.99697, respectively. QPCA-SVR has achieved the smallest RMSEP for four kinds of microplastics, which were 0.26361, 0.80558, 0.37099, and 0.16246, respectively. This work demonstrates QPCA fuses more spectral characteristics and reduces the redundancy of the complex vector, which is beneficial to improve the recognition and regression ability of SVM. The “QPCA enabled FTIR spectroscopy technique” in this study provides a new idea to promote the rapid and accurate determination of microplastics.

Autonomous recognition and positioning of shield segments based on red, green, blue and depth information

Automatic shield segment assembly can reduce intensive labour and increase tunnel boring efficiency. However, insufficient lighting and multi-interference environments significantly affect segment recognition and positioning accuracy. This study develops a highly reliable and low-cost recognition and positioning system, including both hardware and software, for segment markers based on real-time monocular vision and light-insensitive depth measurements. An improved discrete wavelet transform based on the HSV channels and an adaptive multiscale Retinex algorithm with colour recovery are developed to enhance image brightness and colour contrast. Compared to traditional feature matching, support vector machine recognition and classification increase the real-time recognition accuracy. One hundred full-scale and field tests were performed under the original, insufficient, and interferential lighting environments. The recognition time was approximately 0.7 s, and the recognition accuracy was higher than 90%. A grasping experiment verified the feasibility of the system.

Real time railway track crack analysis using multi-level classification

Rail transportation system remains one of the most cost effective and suited means of passenger and goods transportation for both long distance and suburban travel. Hence it is a critical task to ensure regular railway maintenance. This work explores the scope of detecting cracks and also pedestrians in the track by making use of deep learning and image recognition system. This task becomes very challenging as the cracks in the track are varying and the pedestrian can take wide range of poses. This system make use of a conventional Convolutional Neural Network (CNN) along with Support Vector Machine for recognition of crack defective from the dataset. SVM is the binary classifier used, while CNN was used as an automated feature extractor. The dataset was used to train and test the algorithm that was implemented in the proposed model. CNN’s receptive field aids in extracting the most recognizable aspects from these images automatically. The experimental findings show that the suggested framework is successful, with a recognition accuracy of 78% throughout the dataset and a template based lane detection method is used. Lane tracking is done with edge detection and the position of a region of interest in vanishing point in tack image is used for identifying the land departure. This detector on real time video with the region of interest selection and achieve in 23 fps.

A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins

Geographic-label is a remarkable feature for Chinese tea products. In this study, the UHPLC-Q/TOF-MS-based metabolomics approach coupled with chemometrics was used to determine the five narrow-geographic origins of Keemun black tea. Thirty-nine differentiated compounds (VIP > 1) were identified, of which eight were quantified. Chemometric analysis revealed that the linear discriminant analysis (LDA) classification accuracy model is 91.7%, with 84.7% cross-validation accuracy. Three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF) and support vector machine (SVM), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under the curve (AUC). The recognition of RF, SVM and FNN for Keemun black tea from five narrow-geographic origins were 87.5%, 94.44%, and 100%, respectively. Importantly, FNN exhibited an excellent classification effect with 100% accuracy. The results indicate that metabolomics fingerprints coupled with chemometrics can be used to authenticate the narrow-geographic origins of Keemun black teas.

Fuzzy Clustering of Infrared Image Features Based on Lazy Snapping Algorithm

Substation equipment is an important part of the power grid, which undertakes the function of power transmission and conversion and directly affects the operation status of the whole substation and power system. When the substation equipment is in an abnormal working state, the temperature will change. Therefore, the temperature information of the substation equipment is used as the judgment basis to complete the judgment of the working state of the equipment, which can realize the fault diagnosis of the substation equipment and ensure that the power system works in a safe and reliable environment. In this paper, according to the characteristics of the transformer equipment shape stability, the invariant moment is used to extract the infrared image feature of the transformer equipment. The support vector machine is used to complete the classification and recognition of the image. Hu invariant moments and Zernike invariant moments are used to extracting features respectively, and the results of feature extraction are used as training samples to train support vector machines for recognition. Using the Lazy Snapping algorithm to complete the infrared image segmentation processing of substation equipment, the target region is extracted from the background completely, and the segmentation image information is completed. In the experimental test, through the test of the recognition model constructed by invariant moments, it is proved that the recognition accuracy of Zernike invariant moments combined with support vector machine in this paper is higher, and the Lazy Snapping algorithm has obvious advantages in segmentation quality compared with other methods. Therefore, the method of intelligent identification and diagnosis of the thermal fault of substation equipment studied in this paper has important practical significance for the establishment of an online diagnosis system.

FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot

This paper proposes a hierarchical support vector machine recognition algorithm based on a finite state machine (FSM-HSVM) to accurately and reliably recognize the locomotion mode recognition of an exoskeleton robot. As input signals, this method utilizes the angle information of the hip joint and knee joint collected by inertial sensing units (IMUs) on the thighs and shanks of the exoskeleton and the plantar pressure information collected by force sensitive resistors (FSRs) are used as input signals. This method establishes a framework for mode transition by combining the finite state machine (FSM) with the common locomotion modes. The hierarchical support vector machine (HSVM) recognition model is then tightly integrated with the mode transition framework to recognize five typical locomotion modes and eight locomotion mode transitions in real-time. The algorithm not only reduces the abrupt change in the recognition of locomotion mode, but also significantly improves the recognition efficiency. To evaluate recognition performance, separate experiments are conducted on six subjects. According to the results, the average accuracy of all motion modes is 97.106% ± 0.955%, and the average recognition delay rate is only 25.017% ± 6.074%. This method has the benefits of a small calculation amount and high recognition efficiency, and it can be applied extensively in the field of robotics.

EEG Signal Feature Selection Algorithm and Support Vector Machine Model in Patient's Fatigue Recognition.

In March 2020, a cohort of 26 is treated critically ill hospitalized SARS-CoV-2 infected patients who received EEGs to assess unexplained altered mental status, loss of consciousness, or poor arousal and responsiveness. The objective of the present work is to develop a method that is able to automatically determine mental status of vigilance, i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state of mind. Aiming at the EEG feature selection and classification model in the identification of fatigue driving, the discretization algorithm using rough set theory is proposed to select the channel and EEG signal feature quantities. The support vector machine (SVM) is selected as the fatigue driving recognition model, and the risk of fatigue misjudgment is taken as SVM model parameters for model optimization. The experimental results of subjects show that compared with the principal component method, the rough set discretization algorithm selects fewer features, and the compatibility threshold 0.8. The number of features selected among the candidate features is 208. The features selected by different subjects are different and have an impact on the establishment of the support vector machine recognition model. Fatigue misjudgment risk control parameters can adjust the support vector machine recognition model error judgment risk. Even if the present approach is costly in computation time, it allows constructing a decision rule that provides an accurate and fast prediction of the alertness state of an unseen individual.

Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing

In order to build a non-destructive tool for the recognition and classification of bread baking stages, based exclusively on the color changes of the bread crust, the present work aims to propose the development of a Hybrid System (HS) composed of a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM). For training, validation and testing of the HS 374 images of the bread crust were used, distributed over seven baking periods. The results showed that the HS CNN-SVM was able to correctly recognize and classify the baking stages without human intervention, overcoming even models based solely on CNN. In addition, the HS CNN-SVM reduced convergence time and memory consumption, favoring its use in mobile or embedded systems. Finally, the HS CNN-SVM maintained the ability to extract color map characteristics present in CNN, allowing its use in the construction of process control systems for the food industry in which color changes are involved.

Convolutional neural network based on temporal-spatial feature learning for motor imagery electroencephalogram signal decoding

With the advantage of providing more natural and flexible control manner, brain-computer interface systems based on motor imagery electroencephalogram (EEG) have been widely used in the field of human-machine interaction. However, due to the lower signal-noise ratio and poor spatial resolution of EEG signals, the decoding accuracy is relative low. To solve this problem, a novel convolutional neural network based on temporal-spatial feature learning (TSCNN) was proposed for motor imagery EEG decoding. Firstly, for the EEG signals preprocessed by band-pass filtering, a temporal-wise convolution layer and a spatial-wise convolution layer were respectively designed, and temporal-spatial features of motor imagery EEG were constructed. Then, 2-layer two-dimensional convolutional structures were adopted to learn abstract features from the raw temporal-spatial features. Finally, the softmax layer combined with the fully connected layer were used to perform decoding task from the extracted abstract features. The experimental results of the proposed method on the open dataset showed that the average decoding accuracy was 80.09%, which is approximately 13.75% and 10.99% higher than that of the state-of-the-art common spatial pattern (CSP) + support vector machine (SVM) and filter bank CSP (FBCSP) + SVM recognition methods, respectively. This demonstrates that the proposed method can significantly improve the reliability of motor imagery EEG decoding.

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.

As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.

The Broken Wires Identification of Wire Rope Based on Multilevel Filtering Method Using EEMD and Wavelet Analysis

In view of the shortcomings of current wire rope magnetic flux leakage (MFL) detection devices such as large size and low sensitivity, as well as the poor noise reduction effect of MFL data and the low recognition rate of defects, this paper designs a wire rope three-dimensional MFL detection device based on unsaturated magnetic excitation (UME) and proposes a multilevel filtering algorithm. Six types of small gap broken wire defects can be effectively detected by the three-dimensional tunnel magnetoresistive sensor array. The multilevel filtering method based on EEMD and optimal wavelet is applied to three-dimensional UME signal processing, effectively suppressing noise interference. And color processing technology is applied to enhance the characterization of the defect images. A support vector machine recognition network based on the color moment feature vector as input is designed to classify broken wire defects, and the quantitative recognition rate of 98.572% is achieved when the limit error is 0.901%. The experimental results show that the detection device has the advantages of high sensitivity and simple structure and can collect MFL information in three directions, and the filtering algorithm effectively improves the signal-to-noise ratio and can realize the preliminary quantification of broken wire defects.

A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system

Machine Learning (ML) has been applied in so many areas in reason of its robustness, usability, and reliability, mainly in hardware implementation. One of its well-known algorithms is the Support Vector Machine (SVM), the simplest to be applied in hardware because of its mathematical modeling. In this study, we propose the implementation in hardware of SVM multi-class classifiers within the asynchronous paradigm (i.e., without clock signal application) in a 4-stage pipeline architecture. With the purpose to evaluate the proposed architecture behavior, we used a Field Programmable Gate Array (FPGA) device to prototype the circuit. The proposed asynchronous SVM classifier is applied in a Speech Recognition system of 30 classes where a hybrid training algorithm was processed in the Matlab software, known as PSO-SVM training algorithm. Therefore, the training phase was processed in software in reason of its computational load. For the SVM classification phase, we propose, for the first time to the best of our knowledge, an asynchronous pipeline architecture of four stages with Multiply-Accumulator (MAC) unit application and three different control circuits described from Extended Burst-Mode (XBM) and State Transition Graph (STG) specifications, leading to energy-efficient design. In order to validate the SVM recognition results in the speech recognition application, the tests are from 60 speeches and 20 speakers, so it is a diversified and reliable data set of tests. The main goal here was to design a machine learning hardware implementation in a low power application and, through that, to prove that the asynchronous paradigm reduced power. As a result, we obtained a reduced power consumption of 5.72 mW, a fast average response time which was 0.61μs and the most area-efficient circuit (1315 LUTs); the accuracy in recognition success rate was another preoccupation, and it was very successful, 98% of success. Besides, we present comparisons with an asynchronous version of the same SVM datapath and with different synchronous architectures from literature to prove that our novelty is better in power consumption and area size. For hardware applications where low power and high performance are the sought features, the presented architecture revealed the best position when compared to similar works from the recent technical literature for pattern recognition systems.

Support Vector Machine (SVM) for Colorization the Grayscale Image

Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

Research on Feature Recognition of UAV Acoustic Signal Based on SVM

At present, the analysis of UAV flight acoustic signals is mainly based on traditional speech signal processing methods, and has not been analyzed in depth. According to the flight signal of UAV, combined with the aerodynamic characteristics of UAV, the characteristics of UAV’s acoustic signal are analyzed. The three feature extraction algorithms of pitch period, FFT and Mel Cepstral Coefficient (MFCC) are analyzed and compared. Feature extraction is performed, and a support vector machine (SVM) classification algorithm is applied to perform multi-classification model recognition. The measured and experimental results show that based on SVM classification and recognition, the three feature recognition methods all realize the classification of the model. The comprehensive FFT is the best, and the MFCC is the second. The pitch period is not suitable as the feature extraction method alone.

Multi-Weighted Co-Occurrence Descriptor Encoding for Vein Recognition

Despite being highly secure, vein recognition suffers from the high inter-class similarity and intra-class variation resulting from the uncontrolled image capture, making the design of discriminative and robust representation very important. The recent success of convolutional neural network (CNN) for various image understanding tasks makes it a promising method for feature extraction. However, limited variability in small-scale datasets leads to systems derived from the direct training or fine-tuning not transferable and unreliable for practical biometric applications. This motivates the design of a multi-weighted co-occurrence descriptor encoding (MWCDE) model for vein recognition. Instead of directly conducting a feed-forward operation with a pre-trained CNN for obtaining the semantic features from the fully connected layers, co-occurrence features among convolutional filters are modeled first in MWCDE by a simple convolution between an indicator filter in a higher layer with a to-be-reweighted filter in a lower layer, and a redundancy-driven indicator filter selection algorithm is designed for filtering out some ambiguous representations. Second, another hard feature weighting strategy with a binary masking scheme is proposed for discarding noisy background and feature redundancy. The selected high-order descriptors are then embedded and aggregated into the compact feature vectors with a saliency driven spatial weighted Fisher vector algorithm, followed by the introduction of a generalized support vector machine for recognition. Extensive experiments with three benchmark vein datasets demonstrate that the proposed framework can achieve state-of-the-art results, and an additional experiment with the PolyU multispectral palmprint database illustrates its generalization ability. Code is available at (https://github.com/RobinCSIRO/MWCDE-for-Vein-Recognition).

Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

An Exponentially Varying Speed Prediction Method Based on SVM Recognition

The accuracy of vehicle speed prediction has an important influence on the energy management strategy of MPC (Model Prediction Control, MPC), and the exponentially varying prediction model has been widely used in the vehicular speed prediction in future driving cycle. Under a certain prediction horizon, the accuracy of the exponentially varying prediction model mainly depends on the selection of the decay coefficient, but the decay coefficient of the traditional exponentially varying prediction model is fixed. The traditional exponentially varying prediction model cannot meet the prediction accuracy of different driving cycles due to the fixed decay coefficient. In the paper, an exponentially varying prediction based on tunable decay coefficient using SVM (Support Vector Machine, SVM) to recognize the driving cycle is proposed. The simulation results show that the proposed SVM-based exponentially varying speed prediction model can effectively decrease the RMSE (Root Mean Square Error, RMSE) of the vehicle speed prediction, which improves the accuracy of vehicle speed prediction, and provides a foundation for the MPC-based energy management.