Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

Application of wavelet transform in feature extraction and pattern recognition of wideband echoes

A novel approach to extract edge features from wideband echo is proposed. The set of extracted features not only represents the echo waveform in a concise way, but also is sufficient and well suited for classification of non-stationary echo data from objects with different property.The feature extraction is derived from the Discrete Dyadic Wavlet Transform (DDWT) of the echo through the undecimated algorithm. The motivation we use the DDWT is that it is time-shift-invariant which is beneficial for localization of edge, and the wavelet coefficients at larger scale represent the main shape feature of echo, i.e. edge, and the noise and modulated high-frequency components are reduced with scale increased. Some experimental results using real data which contain 144 samples from 4 classes of lake bottoms with different sediments are provided. The results show that our approach is a prospective way to represent wideband echo for reliable recognition of nonstationary echo with great variability.

A SVM-Based Feature Extraction for Face Recognition

Social computing, a cross science of computational science and social science, is affecting people’s learning, work and life recently. Face recognition is going deep into every field of social life, and the feature extraction is particularly important. Linear Discriminant Analysis (LDA) is an effective feature extraction method. However, the traditional LDA cannot solve the nonlinear problem and small sample problem existing in high dimensional space. In this paper, the method of the Support Vector-based Direct Discriminant Analysis (SVDDA) is proposed. It incorporates SVM algorithm into LDA, extends SVM to nonlinear eigenspace, and optimizes eigenvalue to improve performance. Moreover, this paper combines SVDDA with the social computing theory. The experiments were tested on different face datasets. Compared with other existing methods, SVDDA has higher robustness and optimal performance.

Pattern recognitionbased method for radar antideceptive jamming

In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.

Feature extraction of partial discharge in low-temperature composite insulation based on VMD-MSE-IF

Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

Study on the Essence of Optimal Statistically Uncorrelated Discriminant Vectors and Its Application to Face Recognition

A study has been made on the essence of optimal uncorrelated discriminant vectors. A whitening transform has been constructed by means of the eigen decomposition of the population scatter matrix, which makes the population scatter matrix be an identity matrix in the transformed sample space no matter whether the population scatter matrix is singular or not. Thus, the optimal discriminant vectors solved by the conventional linear discriminant analysis (LDA) methods are statistically uncorrelated. The research indicates that the essence of the statistically uncorrelated discriminant transform is the whitening transform plus conventional linear discriminant transform. The distinguished characteristics of the proposed method is that the obtained optimal discriminant vectors are not only orthogonal but also statistically uncorrelated. The proposed method is applicable to all the problems of algebraic feature extraction. The numerical experiments on several facial databases show the effectiveness of the proposed method.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

Robust Feature Extraction for Speaker Recognition Based on Constrained Nonnegative Tensor Factorization

How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method called constrained Nonnegative Tensor Factorization （cNTF）. A novel feature extraction framework based on the cortical representation in primary auditory cortex （A1） is proposed for robust speaker recognition. Motivated by the neural firing rates model in A1, the speech signal first is represented as a general higher order tensor, cNTF is used to learn the basis functions from multiple interrelated feature subspaces and find a robust sparse representation for speech signal. Computer simulations are given to evaluate the performance of our method and comparisons with existing speaker recognition methods are also provided. The experimental results demonstrate that the proposed method achieves higher recognition accuracy in noisy environment.

Size-self-adaptive recognition method of vehicle manufacturer logos based on feature extraction and SVM classifier

Besides their decorative purposes,vehicle manufacturer logos can provide rich information for vehicle verification and classification in many applications such as security and information retrieval.However,unlike the license plate,which is designed for identification purposes,vehicle manufacturer logos are mainly designed for decorative purposes such that they might lack discriminative features themselves.Moreover,in practical applications,the vehicle manufacturer logos captured by a fixed camera vary in size.For these reasons,detection and recognition of vehicle manufacturer logos are very challenging but crucial problems to tackle.In this paper,based on preparatory works on logo localization and image segmentation,we propose a size-self-adaptive method to recognize vehicle manufacturer logos based on feature extraction and support vector machine(SVM)classifier.The experimental results demonstrate that the proposed method is more effective and robust in dealing with the recognition problem of vehicle logos in different sizes.Moreover,it has a good performance both in preciseness and speed.

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis （PCA）. After obtaining the region of interest （ROI） for each finger image in the pre- processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve （K-L） transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Research on Intelligent Identification of PD Patterns Based on the Fingerprint Features

Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J4 value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J5 value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.

Hybrid Features for an Arabic Word Recognition System

This research proposes and implements an Arabic Sub-Words Recognition System （ASWR）. The system focuses on employing a combination of statistical and structural features to provide complete pattern＇s description and enhances the recognition rate. Support Vector Machines （SVMs） is utilized as a promising pattern recognition tool. In addition to that, the problems of dots and holes are solved in a completely different way from the ones previously employed. The proposed system proceeds in several phases as follows： （1） image acquisition, （2） binarisation, （3） morphological processing, （4） feature extraction, which includes statistical features, i.e., moment invariants, and structural features, i.e., dot number, dot position, and number of holes, features, and （5） classification, using multi-class SVMs and applying a one-against-all technique. The proposed system has been tested using different sets of words and subwords and has achieved a nearly 98.90% recogiaition rate. Comparative results with NNs are also presented.

An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.

Research on Feature Fusion Technology of Fruit and Vegetable Image Recognition Based on SVM

In order to improve the accuracy and stability of fruit and vegetable image recognition by single feature, this project proposed multi-feature fusion algorithms and SVM classification algorithms. This project not only introduces the Reproducing Kernel Hilbert space to improve the multi-feature compatibility and improve multi-feature fusion algorithm, but also introduces TPS transformation model in SVM classifier to improve the classification accuracy, real-time and robustness of integration feature. By using multi-feature fusion algorithms and SVM classification algorithms, experimental results show that we can recognize the common fruit and vegetable images efficiently and accurately.

RESEARCH ON TWO-DIMENSIONAL LDA FOR FACE RECOGNITION

The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear discriminant analysis method makes full use of not only the row and the column direc-tion information of face images but also the discriminant information among different classes. The method is evaluated using the Nanjing University of Science and Technology (NUST) 603 face database and the Aleix Martinez and Robert Benavente (AR) face database. Experimental results show that the method in the letter is feasible and effective.

RESEARCH ON FACE RECOGNITION BASED ON IMED AND 2DPCA

This letter proposes an effective method for recognizing face images by combining two-Dimen- sional Principal Component Analysis (2DPCA) with IMage Euclidean Distance (IMED) method. The proposed method is comprised of four main stages. The first stage uses the wavelet decomposition to extract low fre- quency subimages from original face images and omits the other three subimages. The second stage concerns the application of IMED to face images. In the third stage, 2DPCA is employed to extract the face features from the processed results in the second stage. Finally, Support Vector Machine (SVM) is applied to classify the extracted face features. Experimental results on the AR face image database show that the proposed method yields better recognition performance in comparison with the 2DPCA method that is not combined with IMED.

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

Fabric Pattern Elements Retrieval Based on Cosine Transform

Fabric pattern contains many types of the available pattern elements, which not only can be used for the researchers, but also as the material for the designer. But existing method focus on the complete image retrieval, therefore lack methods of retrieving pattern elements. This article proposes a pattern elements retrieval algorithm based on cosine transform. Firstly, automatically segment the patterns according to size and location and filter the similar primary patterns, then, through cosine transform, analyze elements features in DCT domain, extract amplitude frequency and phase frequency. We employ 2-norm to measure the similarity, search 10 similar pattern elements in the sample library and save them in the design resources library. Experiment results indicate that this algorithm performs well while used in palace costume and carpet patterns, and got more than 75% of the average recall in 100 times experiments