Noise Effects on Feature Mining Non-Parametric Supervised Feature Extraction Techniques

Farid Muhammad Imran 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.12

In this paper two famous and commonly used feature mining non-parametric supervised feature extraction techniques (NSFETs) called Non-parametric Weighted Feature Extraction (NWFE) and Decision Boundary Feature Extraction (DBFE) are analyzed to see their efficiency in the presence of noise. In particularly these feature extraction techniques are used in classification as they give better classification accuracy. This study reveals that NSFETs are very sensitive to noise because of which the number of features increases and we get low classification accuracy. In order to see the behavior of NSFETs, spatial and spectral information from hyperspectral image classification is used. The experimental results show that in the presence of noise, spectral information is much more effected than the spatial information when features are extracted using the NSFETs. It is also examined that NWFE is more affected by noise than DBFE. The linear filtering technique is used just before the classifier in order to mitigate the noise effects in NSFETs. Using linear filtering just before the classifier does improve the final classification accuracy but with high number of spatial and spectral features. This does not satisfy the one of the main purpose of feature extraction and that is feature reduction.

An improved semi-supervised dimensionality reduction using feature weighting: Application to sentiment analysis

Elsevier 2018 expert systems with applications Vol.109 No.-

<P><B>Abstract</B></P> <P>Analyzing a large number of documents for sentiment analysis entails huge complexity and cost. To alleviate this burden, dimensionality reduction has been applied to documents as a preprocessing step. Among dimensionality reduction algorithms, compared with feature selection, feature extraction can reduce information loss and achieve a higher discriminating power in sentiment classification. However, feature extraction suffers from lack of interpretability and many nonlinear extraction methods, which generally outperform linear methods, are not applicable for sentiment classification because of the characteristics that only provide corresponding low-dimensional coordinates without mapping. Therefore, this research proposes an improved semi-supervised dimensionality reduction framework that simultaneously preserves the advantages of feature extraction and addresses the drawbacks for sentiment classification. The proposed framework is mainly based on linear feature extraction providing mapping and feature weighting is applied before feature extraction. Feature weighting and extraction are conducted in a semi-supervised manner so that both label information and structural information of data can be considered. The superiority of both feature weighting and feature extraction was verified by conducting extensive experiments in six benchmark datasets.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A semi-supervised feature extraction combines with feature weighting is proposed. </LI> <LI> Feature weighting considers both co-occurrence of terms and label of documents. </LI> <LI> The polarity scores defined in SentiWordNet are reflected in the feature weights. </LI> <LI> Six datasets are used to validate the enhanced performance of the proposed method. </LI> </UL> </P>

Fractional Differentiation-based Image Feature Extraction

Xiangwei Xu,Fang Dai,Wenyan Guo,Jianmin Long 보안공학연구지원센터(IJSIP) 2014 International Journal of Signal Processing, Image Vol.7 No.6

Two novel methods for image feature extraction based on fractional differentiation are presented in this paper. The first method is the feature extraction of fusing multi-direction CRONE operators. In this method, the fractional differential CRONE mask is generalized to eight directions at first for extracting image features; then the extracted features are tested by the statistic method and fused by the gradient ratio, so that the outlines of the objects in the image are obtained. In order to extract the detail feature information in the image effectively, the second method, the ‘S+Z’ extraction combined with the space-filling curves, is presented. By introducing the space-filling curves, the ‘S’ curve and the ‘Z’ curve, and making full use of the neighborhood information of image pixels, the detailed features of the objects in the image are obtained. The experiment results show that our methods can obtain satisfactory image features.

An Improved Image Registration Based on Nonsubsampled Contourlet Transform and Zernike Moments

Rui Ding,Ziyan Song,Jin Tang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.6

Feature-based registration is an effective and most widely used image registration method currently. It includes three critical steps, feature extraction, feature matching and transformation parameters estimation. This paper mainly explores the first two steps. In one of Chahira Serief’s paper about image registration, feature points extraction based on nonsubsampled contourlet transform (NSCT) was proposed and feature points matching based on Zernike moments was adopted. The registration accuracy and robustness of his algorithm are acceptable, but it can still be improved. In this paper, an improved scheme of this registration algorithm is proposed. The rotation invariance of NSCT-based feature points extraction is improved, which is beneficial to extract homologous feature points. And the reliability and effectiveness of Zernike moments-based feature points matching are improved, which can improve the matching accuracy. The improved registration algorithm can realize registration of images related by larger scaling, rotation and translation transformation. The simulation results show that the registration robustness is further improved, and the registration accuracy is still high.

Facial Feature Extraction with Its Applications

Lee, Minkyu,Lee, Sangyoun International Society for Simulation Surgery 2015 Journal of International Society for Simulation Su Vol.2 No.1

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

FEROM: Feature Extraction and Refinement for Opinion Mining

Hana Jeong,Dongwook Shin,최중민 한국전자통신연구원 2011 ETRI Journal Vol.33 No.5

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.

주파수에 따른 감쇠계수 변화량을 이용한 해저 퇴적물 특징 추출 알고리즘

이기배(Kibae Lee),김주호(Juho Kim),이종현(Chong Hyun Lee),배진호(Jinho Bae),이재일(Jaeil Lee),조정홍(Jung Hong Cho) 대한전자공학회 2017 전자공학회논문지 Vol.54 No.1

본 논문에서는 해저 퇴적물 분류를 위한 특징 추출 기법을 제안하고 검증한다. 기존 연구에서는 주파수의 영향이 없는 반사계수를 이용하여 퇴적물을 분류해 왔다. 그러나 해저 퇴적물의 음향 감쇠계수는 주파수의 함수이며 퇴적 성분에 따라 서로 다른 특성을 나타낸다. 따라서 주파수에 따른 감쇠계수 변화량을 이용하여 특징벡터를 생성하였다. 감쇠계수 변화량은 Chirp 신호에 의해 생성된 두 번째 층 반사신호를 이용하여 추정한다. Chirp 신호의 다중대역 특징이 다차원 벡터를 형성하기 때문에 기존의 방법에 비해 우수한 특성을 갖는다. 반사계수에 의한 분류 성능과 비교하기 위해 선형 판별 분석법 (LDA, Linear Discriminant Analysis)를 이용하여 차원을 축소하였다. Biot 모델을 이용하여 모의실험 환경을 구축하고 Fisher score와 MLD (Maximum Likelihood Decision)를 기반의 분류 정확도를 이용해 제안된 특징을 평가하였다. 그 결과, 제안된 특징은 반사계수에 비해 높은 변별력을 보이며, 측정 및 깊이 추정오차에도 강인한 특성을 보였다. In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

Feature Extraction and Matching Algorithms to Improve Localization Accuracy for Mobile Robots

Sin-Won Kang,Sang-Hyeon Bae,Tae-Yong Kuc 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

Localization of indoor mobile robots is an important field in Simultaneous Localization and Mapping (SLAM). SLAM is a technology that generates map and estimates the current locations as robot explore random space. So, that is commonly used in indoor environments where GPS is not working. We propose the method of feature extraction and feature matching for localization. Features are represented wall and corner in line and point. We extract lines and corner points with observed data by 2D lidar sensor and match extracted features with a stored feature in the map. Finally, we show to increase the accuracy of localization by calculating differences between coordinates of matched features.

Optimum feature selection for SHM of benchmark structures using efficient AI mechanism

Ramin Ghiasi,Mohammad Reza Ghasemi,H.T. Chan 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.4

Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process.

Two Dimensional Slow Feature Discriminant Analysis via L_2,1 Norm Minimization for Feature Extraction

( Xingjian Gu ),( Xiangbo Shu ),( Shougang Ren ),( Huanliang Xu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.7

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via L_2,1 norm minimization (2DSFDA-L_2,1) is proposed. 2DSFDA-L_2,1 integrates L_2,1 norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, L_2,1 norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed L_2,1 nonlinear model into a linear regression type. Additionally, 2DSFDA-L_2,1 is extended to a bilateral projection version called BSFDA-L_2,1. The advantage of BSFDA-L_2,1 is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed 2DSFDA-L_2,1/BSFDA-L_2,1 can obtain competitive performance.