Comparison Of Feature Extraction Techniques Research Articles

A comparison of feature extraction techniques for speech emotion identification

A comparison of feature extraction techniques for speech emotion identification

A Review on EMG Signal Classification and Applications

Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed.

Comparison of Feature Extraction Techniques for Pattern Classification

Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

Towards an objective comparison of feature extraction techniques for automatic speaker recognition systems

A common limitation of the previous comparative studies on speaker-features extraction techniques lies in the fact that the comparison is done independently of the used speaker modeling technique and its parameters. The aim of the present paper is twofold. Firstly, it aims to review the most significant advancements in feature extraction techniques used for automatic speaker recognition. Secondly, it seeks to evaluate and compare the currently dominant ones using an objective comparison methodology that overcomes the various limitations and drawbacks of the previous comparative studies. The results of the carried out experiments underlines the importance of the proposed comparison methodology.

A Comparison of Feature Extraction Techniques for Delamination of CFRP Using Eddy Current Pulse-Compression Thermography

CFRP (carbon fiber reinforced plastic) has replaced conventional metallic materials in many industrial applications because of its outstanding mechanical performance such as high strength to ratio and resistance of fatigue. During the service life of the aerospace composite component, delamination left without detection can cause a sudden breakdown of the structure. Eddy current pulse-compression thermography (ECPuCT) is an emerging technique that combines traditional ECPT and pulse compression techniques. In this work, feature extraction techniques of impulse response have been exploited in terms of principal component analysis (PCA), kernel principal component analysis (K-PCA) and independent component analysis (ICA). Each technique is evaluated using SNR as the index to compare their performance. The results indicate that Kernel-PCA performs better than PCA and ICA based features when dealing with delamination ranged from defect#1(0.46mm) to defect#9 (2.30mm).

Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques

Background and ObjectiveSurgical skill assessment aims to objectively evaluate and provide constructive feedback for trainee surgeons. Conventional methods require direct observation with assessment from surgical experts which are both unscalable and subjective. The recent involvement of surgical robotic systems in the operating room has facilitated the ability of automated evaluation of the expertise level of trainees for certain representative maneuvers by using machine learning for motion analysis. The features extraction technique plays a critical role in such an automated surgical skill assessment system. MethodsWe present a direct comparison of nine well-known feature extraction techniques which are statistical features, principal component analysis, discrete Fourier/Cosine transform, codebook, deep learning models and auto-encoder for automated surgical skills evaluation. Towards near real-time evaluation, we also investigate the effect of time interval on the classification accuracy and efficiency. ResultsWe validate the study on the benchmark robotic surgical training JIGSAWS dataset. An accuracy of 95.63, 90.17 and 90.26% by the Principal Component Analysis and 96.84, 92.75 and 95.36% by the deep Convolutional Neural Network for suturing, knot tying and needle passing, respectively, highlighted the effectiveness of these two techniques in extracting the most discriminative features among different surgical skill levels. ConclusionsThis study contributes toward the development of an online automated and efficient surgical skills assessment technique.

Comparison of feature extraction techniques for classification of hardwood species

The texture of an image plays an important role in identification and classification of images. The hardwood species of an image contains four key elements namely: vessels (popularly known as pores in cross-section view), fibres, parenchyma's and rays, useful in its identification and classification. Further, the arrangements of all these elements posses texture rich features. Thus, in this work investigation of existing texture feature extraction techniques for the classification of hardwood species have been done. The texture features are extracted from greyscale images of hardwood species to reduce the computational complexity. Further, linear support vector machine (SVM), radial basis function (RBF) kernel SVM, random forest (RF) and linear discriminant analysis (LDA) have been employed as classifiers to investigate the efficacy of the texture feature extraction techniques. The classification accuracy of the existing texture descriptors has been compared. Further, principal component analysis (PCA) and minimal-redundancy-maximal-relevance (mRMR) feature selection method is employed to select the best subset of feature vector data. The PCA reduced feature vector data of co-occurrence of adjacent local binary pattern (CoALBP24) texture feature extraction technique has attained maximum classification accuracy of 96.33 ± 1.14% with the help of LDA classifier.

Comparison of feature extraction techniques for classification of hardwood species

Comparison of feature extraction techniques for classification of hardwood species

A comparison of feature extraction techniques for malware analysis

The manifold growth of malware in recent years has resulted in extensive research being conducted in the domain of malware analysis and detection, and theories from a wide variety of scientific knowledge domains have been applied to solve this problem. The algorithms from the machine learning paradigm have been particularly explored, and many feature extraction methods have been proposed in the literature for representing malware as feature vectors to be used in machine learning algorithms. In this paper we present a comparison of several feature extraction techniques by first applying them on system call logs of real malware, and then evaluating them using a random forest classifier. In our experiment the HMM-based feature extraction method outperformed the other methods by obtaining an F-measure of 0.87. We also explored the possibility of using ensembles of feature extraction methods, and discovered that combination of HMM-based features with bigram frequency features improved the F-measure by 1.7%.

Global location of mobile robots using Artificial Neural Networks in omnidirectional images

This paper presents a comparison of Mobile Robots localization methods through Artifical Neural Networks in omnidirectional images. After an overview about Mobile Robotics, this work focuses on omnidirectional vision. The motivation for this work is the implementation and comparison of feature extraction techniques that can be used in omnidirectional images seeking invariance to rotation and building descriptors that can be used in Neural Networks. Five feature extraction techniques with their adaptations for omnidirectional image were presented and compared. The results were shown in order to choose the most suitable feature extractor for this application. The feature extractorsare evaluated with respect to time processing and quality of scene description (accuracy of Artificial Neural Network) The results are satisfactory and elect GIST descriptor as the most suitable for the application.