Popular Feature Extraction Methods Research Articles

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture feature extraction, shape feature extraction and intrinsic feature extraction. Subsequently, the signal of each frame image was recognized to track the vibration source. To verify the effect of the proposed method, several experiments were carried out to compare it with popular and traditional approaches. The results show that: Hard drive space is greatly conserved by saving the distributed vibration signals as images; the proposed particle filter is a desirable way to screen the vibration signals for monitoring; the integrated feature extraction outperforms the individual extraction methods for texture features, shape features and intrinsic features; the proposed method has a better effect than other popular integrated feature extraction methods; and, the signal source tracking method has little impact on the positioning accuracy of the vibration source. The research findings provide important insights into the source tracking of Φ-OTDR signals.

Local Binary Pattern Circuit Generator With Adjustable Parameters for Feature Extraction

In the field of computer vision, local binary pattern (LBP) is one of the most popular feature extraction method and has been used in many object detection frameworks. To efficiently extract LBP features in high-resolution images, hardware architecture is needed to disperse CPU burden and to improve the entire object detection performance. In this paper, a hardware implementation of an approximated LBP method with adjustable parameters is introduced. For simulation, Taiwan Semiconductor Manufacturing Company <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> technology is used to implement the LBP hardware, and the hardware can achieve 500 MHz with lower gate count than previous study. The proposed LBP circuit is applied to the pedestrian classification application and the evaluation results show that the approximated LBP values generated by our circuit can achieve comparable classification accuracy with the primitive LBP method. Additionally, the proposed LBP hardware provides adjustable parameters to fit different applications while requires fewer hardware costs as compared with the existing work.

A STUDY ON PRE AND POST PROCESSING OF FINGERPRINT THINNED IMAGE TO REMOVE SPURIOUS MINUTIAE FROM MINUTIAE TABLE

In Fingerprint recognition, after the initial preprocessing, the feature is extracted from the Fingerprint thinned image. Extraction of crucial and beneficial capabilities or features of interest from a fingerprint image is an essential venture during recognition. Feature extraction algorithms pick handiest or only applicable features important for enhancing the performance of matching and recognition rate and outcomes with the feature vector. The feature extraction algorithms or techniques require only relevant features like minutiae details and do not require any background details or domain-specific details. They need to be smooth or easy to compute with a purpose to gain a viable or practicable technique for a huge image series. Minutiae details or fingerprint ridge ending or bifurcation details using skeletonized or thinning approach is a very popular method for feature extraction. The preprocessed thinned image is further post-processed to remove some false minutiae from minutiae table and which is generated through crossing number theory. One more purpose of post-processing is to reduce the number of minutiae points by removing false minutiae structures like spurs, ride breaks, short ridge, holes or islands, bridges, and ladders. In this paper w × w window neighborhood is considered for each minutia in Minutiae Table. Minutiae Table contains Ridge ending or bifurcation code as 1 or 3 with its location details means x and y position in two columns and the sum of these details as its fourth column. These Minutiae tables are used for generating Fingerprint Hash code, which can be used as index-or identity key in order to uniquely identify an individual person or as one factor in Multifactor Authentication Model.

An integrated optimisation algorithm for feature extraction, dictionary learning and classification

Abstract Recently, sparse representation-based classification (SRC) has received much attention for its robustness in pattern recognition. Because SRC deals with high-dimensional data, huge computational resources are required to compute sparse representations of query samples, which renders SRC solutions of high-dimensional problems infeasible. To overcome this problem, an integrated optimisation algorithm is proposed to implement feature extraction, dictionary learning and classification simultaneously. First, to obtain sparse representation coefficients, a sparsity preserving embedding map is learnt to reduce the dimensionality of the data. Second, an optimal dictionary is adaptively obtained from the training data to reduce trivial information. Third, the training samples are reclassified using sparse representation coefficients. Furthermore, the integrated learning algorithm is extended to unsupervised learning. Experimental results clearly demonstrate that the proposed method achieves better performance than several popular feature extraction and classification methods.

An effective two-dimensional linear discriminant analysis with locality preserving approach for image recognition

Linear discriminant analysis (LDA) is one of the most popular feature extraction methods in image recognition. Nevertheless, LDA always suffers from some undesired behaviors caused by globality, namely ignoring local geometric structure of images. To fully capture the geometric information, we propose a novel two-dimensional linear discriminant analysis with locality preserving (2DLP-LDA) approach, which can cover both within-class and between-class local geometric information. 2DLP-LDA re-characterizes the within-class scatter matrix by employing locality preserving projection technique and re-characterizes the between-class scatter matrix by introducing a Gaussian weighting function, making the samples from the same class moderately cluster, while the samples from different classes properly distribute in the reduced subspace. Encouraging experimental results on FERET face database as well as SEU bovine iris database show the effectiveness of the proposed approach.

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.

Spatial-spectral locality preserving projection for hyperspectral image classification with limited training samples

ABSTRACTLocality preserving projection (LPP) is a popular unsupervised feature extraction (FE) method. In this paper, the spatial-spectral LPP (SSLPP) method is proposed, which uses both the spectral and spatial information of hyperspectral image (HSI) for FE. The proposed method consists of two parts. In the first part, unlabelled samples are selected in a spatially homogeneous neighbourhood from filtered HSI. In the second part, the transformation matrix is calculated by an LPP-based method and by using the spectral and spatial information of the selected unlabelled samples. Experimental results on Indian Pines (IP), Kennedy Space Center (KSC), and Pavia University (PU) datasets show that the performance of SSLPP is superior to spectral unsupervised, supervised, and semi-supervised FE methods in small and large sample size situations. Moreover, the proposed method outperforms other spatial-spectral semi-supervised FE methods for PU dataset, which has high spatial resolution. For IP and KSC datasets, spectral regularized local discriminant embedding (SSRLDE) has the best performance by using spectral and spatial information of labelled and unlabelled samples, and SSLPP is ranked just behind it. Experiments show that SSLPP is an efficient unsupervised FE method, which does not use training samples as preparation of them is so difficult, costly, and sometimes impractical. SSLPP results are much better than LPP. Also, it decreases the storage and calculation costs using less number of unlabelled samples.

Fuzzy clustering-based feature extraction method for mental task classification

A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman’s statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.

Spare L1-norm-based maximum margin criterion

Maximum margin criterion (MMC) is a popular method for dimensionality reduction or feature extraction. MMC can alleviate the small size sample (SSS) problem encountered by linear discriminant analysis (LDA) and extract more discriminant vectors than LDA. However, the objective function of MMC is derived from L2-norm, which makes MMC be sensitive to noise and outliers. Besides, the basis vectors of MMC are dense, which makes it hard to explain the obtained features. To address the drawbacks of MMC, in this paper, we propose a novel sparse L1-norm-based maximum margin criterion (SMMC-L1). L1-norm rather than L2-norm is used in the objective function of SMMC-L1. Besides, L1-norm is also used as a lasso penalty to regularize the basis vectors. An iterative algorithm for solving SMMC-L1 is proposed. Experiment results on some databases show the effectiveness of the proposed SMMC-L1.

Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

Wavelet Kernel Local Fisher Discriminant Analysis With Particle Swarm Optimization Algorithm for Bearing Defect Classification

Feature extraction and dimensionality reduction (DR) are necessary and helpful preprocessing steps for bearing defect classification. Linear local Fisher discriminant analysis (LFDA) has recently been developed as a popular method for feature extraction and DR. However, the linear method tends to give undesired results if the samples between classes are nonlinearly separated in the input space. To enhance the performance of LFDA in bearing defect classification, a new feature extraction and DR algorithm based on wavelet kernel LFDA (WKLFDA) is presented in this paper. Herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. To seek the optimal parameters for WKLFDA, particle swarm optimization (PSO) is used; as a result, a new PSO-WKLFDA algorithm is proposed. The experimental results for the synthetic data and measured vibration bearing data show that the proposed WKLFDA and PSO-WKLFDA outperform other state-of-the-art algorithms.

English

Content-based image retrieval (CBIR) is a technique which uses visual content to search and compare images from large scale image databases according to the interests of users. Visual content of the image like color, shape, texture and spatial layout are widely used in CBIR. Texture features has been successfully used to provide a meaningful tool for searching image databases, as texture images generally contain unique visual patterns or spatial arrangements of pixels, so that describing textures, gray- level or color alone, may not yield to classify similar ones. In this paper we mainly concentrate on Spectral methods which extract texture features from the energy distribution in the frequency domain. The most popular spectral feature extraction methods are Fourier, wavelet, and Gabor filtering. In the first process it will extract the features from query and database images to a distinguishable extent using Gabor and wavelet Transforms. In the second process it involves matching these features to yield a result that is visually similar by feature matching process.

Obtaining Cross Modal Similarity Metric with Deep Neural Architecture

Analyzing complex system with multimodal data, such as image and text, has recently received tremendous attention. Modeling the relationship between different modalities is the key to address this problem. Motivated by recent successful applications of deep neural learning in unimodal data, in this paper, we propose a computational deep neural architecture, bimodal deep architecture (BDA) for measuring the similarity between different modalities. Our proposed BDA architecture has three closely related consecutive components. For image and text modalities, the first component can be constructed using some popular feature extraction methods in their individual modalities. The second component has two types of stacked restricted Boltzmann machines (RBMs). Specifically, for image modality a binary-binary RBM is stacked over a Gaussian-binary RBM; for text modality a binary-binary RBM is stacked over a replicated softmax RBM. In the third component, we come up with a variant autoencoder with a predefined loss function for discriminatively learning the regularity between different modalities. We show experimentally the effectiveness of our approach to the task of classifying image tags on public available datasets.

Multiset Canonical Correlations Using Globality Preserving Projections With Applications to Feature Extraction and Recognition

Multiset features extracted from the same patterns always represent different characteristics of data. Thus, it is very valuable to perform the extraction on multiple feature sets. This paper addresses the issue of multiset correlation feature extraction (MCFE) in multiple feature representations. A novel method is proposed to carry out the MCFE for classification, called multiset canonical correlations using globality-preserving projections (MCC-GPs), which can perform joint dimensionality reduction for high-dimensional data. MCC-GP integrates correlational characteristics of feature pairs and global geometric information of data in the transformed low-dimensional space. This makes MCC-GPs have better discriminant ability than a previous method proposed by the authors, called multiset integrated canonical correlation analysis (MICCA), which only considers correlations for recognition tasks. Furthermore, MCC-GP can subsume two popular feature extraction methods into its framework under some constraints. This also provides a new insight for these two methods. The proposed method is applied to pattern recognition and examined using the COIL-100 and ETH-80 object databases and AR, CMU PIE, and Yale face databases. Extensive experimental results show that MCC-GP outperforms MICCA and multiset canonical correlation analysis in terms of classification accuracy and efficiency.

A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

SUMMARY Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC

Bayesian common spatial patterns for multi-subject EEG classification

Multi-subject electroencephalography (EEG) classification involves algorithm development for automatically categorizing brain waves measured from multiple subjects who undergo the same mental task. Common spatial patterns (CSP) or its probabilistic counterpart, PCSP, is a popular discriminative feature extraction method for EEG classification. Models in CSP or PCSP are trained on a subject-by-subject basis so that inter-subject information is neglected. In the case of multi-subject EEG classification, however, it is desirable to capture inter-subject relatedness in learning a model. In this paper we present a nonparametric Bayesian model for a multi-subject extension of PCSP where subject relatedness is captured by assuming that spatial patterns across subjects share a latent subspace. Spatial patterns and the shared latent subspace are jointly learned by variational inference. We use an infinite latent feature model to automatically infer the dimension of the shared latent subspace, placing Indian Buffet process (IBP) priors on our model. Numerical experiments on BCI competition III IVa and IV 2a dataset demonstrate the high performance of our method, compared to PCSP and existing Bayesian multi-task CSP models.

THE ACCESS CONTROL SYSTEM BASED ON LINEAR DISCRIMINANT ANALYSIS

Face recognition represents an important computer v ision domain that has been researched in the last decades. The objective of this research is to devel op an Access Control System based on Face Recognition using Linear Discriminant Analysis (LDA) method. The analysis is done by making an application of face recognition using LDA for extracting features of th e face and the output for controlling relay as a si mulation of a door using AVR Microcontroller. The result ach ieved by the application is a face recognition syst em using LDA can obtain excellent result that is 95% s uccess rate in face recognition with the image of a face that was tested by 40 people, instead of the previo usly popular feature extraction methods such as PCA. Based on the experimental results, attendance syste m based on face recognition using LDA obtain better result compared with PCA based face recognition.

Evaluation of Distance Measures For NMF-Based Face Image Applications

Non-negative matrix factorization (NMF) is an increasingly popular feature extraction method. Since it is designed to fit training samples using linear combination of non-negative basis vectors, it is particular suitable for imageapplications as it affords intuitive localized interpretations.However, in this space defined by the NMF basis images,there has not been any systematic research to identifysuitable distance measure for NMF-based data classification. In this paper, the performance of 19 distance measuresbetween feature vectors is evaluated based on the result ofthe NMF algorithm for face recognition, which include most of the standard distance measures used in face recognition,as well as two new non-negative vector similarity coefficient-based (NVSC) distances that we recommend for use in NMF-based pattern recognition. Recognition experiments are performed using the CMU AMP Face Expression database, CBCL2 database, MIT-CBCL database, YaleB database, and FERET database. We also compared the performance of NMF with Eigenface method and showed that the NMF algorithm using the NVSC distance yielded the best recognition results.

Comparison of SIFT and SURF Methods for Use on Hand Gesture Recognition based on Depth Map

In this paper a comparison between two popular feature extraction methods is presented. Scale-invariant feature transform (or SIFT) is the first method. The Speeded up robust features (or SURF) is presented as second. These two methods are tested on set of depth maps. Ten defined gestures of left hand are in these depth maps. The Microsoft Kinect camera is used for capturing the images [1]. The Support vector machine (or SVM) is used as classification method. The results are accuracy of SVM prediction on selected images.

An efficient diagnosis method for data mining on single PD pulses of transformer insulation defect models

Reviewing the various Partial Discharges (PD data mining researches which have been reported so far, this study compares the performance of different feature spaces and different classifiers employed for PD classification in insulation condition monitoring of power transformers. In this process, first a knowledge basis is developed through construction of 4 different types of PD models in the high voltage laboratory. Background noise is considered as one class in this knowledge basis. The high frequency time domain current signals of high voltage equipment are captured over one power frequency cycle. The single PD activities within this captured signal are extracted by application of a threshold-based method. Four popular feature extraction methods i.e. Statistical, texture, FFT and Cepstral features are applied on these recorded extracted PD signals. To distinguish the different PD types, three conceptually different classifier types, Neural Network, Decision Tree, and k-nearest neighbours, are applied on the recorded feature spaces. Using Bayesian theory, a performance analysis is carried out to find whether the classifiers are over-fitted or not. Although, the most reliable data mining tool found to be a combination of a Cepstral feature space, and neural network classifier however, since the statistical features can be computed very fast it is employed in this work. Next, it is proposed to use a cascade PD identifier to find whether the detected signal is noise or not. And if it is PD, employing Cepstral feature space knowledge-basis, its type is identified.