Two-dimensional Principal Component Analysis Research Articles

Average gait differential image based human recognition.

The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI) is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI), AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA) is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.

Optimization Method for Crop Growth Characteristics Based on Improved Locality Preserving Projection

Locality preserving projection (LPP) retains only partial information, and category information of samples is not considered, which causes misclassification of feature extraction. An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. Firstly, preliminary dimensionality reduction of sample data is constructed by using two-dimensional principal component analysis (2DPCA) to retain the spatial information. Then, two optimized subgraphs are defined to describe the neighborhood relation between different categories of data. Finally, feature parameters set are obtained to extract local information of samples by improved LPP algorithm. The experiments show that the improved LPP algorithm has good adaptability, and the highest SVM classification accuracy rate of this method can reach more than 96%. Compared with other methods, the improved LPP has superior optimized performance in terms of multidimensional data analysis and optimization.

Two-dimensional PCA highlights the differentiated antitumor and antimicrobial activity of methanolic and aqueous extracts of Laurus nobilis L. from different origins.

Natural matrices are important sources of new antitumor and antimicrobial compounds. Species such as Laurus nobilis L. (laurel) might be used for this purpose, considering its medicinal properties. Herein, in vitro activity against human tumor cell lines, bacteria, and fungi was evaluated in enriched phenolic extracts. Specifically, methanol and aqueous extracts of wild and cultivated samples of L. nobilis were compared considering different phenolic groups. Principal component analysis (PCA) was applied to understand how each extract acts differentially against specific bacteria, fungi, and selected human tumor cell lines. In general, the extract type induced the highest differences in bioactivity of laurel samples. However, from the PCA biplot, it became clear that wild laurel samples were higher inhibitors of tumor cell lines (HeLa, MCF7, NCI-H460, and HCT15). HepG2 had the same response to laurel from wild and cultivated origin. It was also observed that methanolic extracts tended to have higher antimicrobial activity, except against A. niger, A. fumigatus, and P. verrucosum. The differences in bioactivity might be related to the higher phenolic contents in methanolic extracts. These results allow selecting the extract type and/or origin with highest antibacterial, antifungal, and antitumor activity.

A mass classification and image retrieval model for mammograms

In this paper, a breast tissue density classification and image retrieval model is studied and a model for the data reduction is presented. This model is based on two-directional two-dimensional principal component analysis ((2D)2PCA) technique, and a support vector machine (SVM) with the radial basis function (RBF) for mammographic images classification and retrieval. The model is formed based on breast density, according to the categories defined by the breast imaging-reporting and data system (BIRADS) which is a standard on the assessment of mammographic images and is tested on the Mammographic Image Analysis Society (MIAS) database. The five-fold cross-validation has been used for the parameters selection in SVM to avoid the over-fitting error in the data classification. The average precision rates of the model are in the range from 87·34% to 99·12%.

IL-17 and TNF Synergistically Modulate Cytokine Expression while Suppressing Melanogenesis: Potential Relevance to Psoriasis

Inflammation-associated pigmentation changes are extremely common, but the etiology behind this clinical observation remains elusive. Particularly, it is unclear how the myriad of cytokines known to be involved in inflammatory skin processes affect epidermal melanocytes. We sought to determine how IL-17 and TNF influence normal human melanocytes, as these two cytokines have been implicated in various skin diseases. IL-17 and TNF jointly stimulated broad inductions of cytokines , including melanoma mitogens CXCL1 and IL-8. Moreover, IL-17 and TNF synergistically inhibited pigmentation-related signaling and melanin production, and induced keratinocytes production of β-defensin 3, an antagonist for melanocortin-receptor 1. When analyzing psoriasis lesions that are known to over express IL-17 and TNF, we observed an increase in melanocyte number and a simultaneous decrease in pigmentation signaling. Furthermore, therapeutic neutralization of TNF and IL-17 with mAbs results in a rapid recovery of pigment gene expression in psoriasis lesions. These results demonstrate that IL-17 and TNF can impact both the growth and pigment production of melanocytes, which may contribute to the pigmentation changes associated with psoriasis. These findings may allow the development of novel therapeutics for pigmentary disorders and bring new insights into the immune milieu surrounding melanocytes and related neoplasms.

Speaker adaptation using probabilistic linear discriminant analysis for continuous speech recognition

The application of probabilistic linear discriminant analysis (PLDA) to speaker adaptation for automatic speech recognition based on hidden Markov models is proposed. By expressing the set of acoustic models of each of the training speakers in a matrix and treating each column as a sample, the small sample problem that can be encountered in PLDA if only one sample is available for each training speaker is overcome. In the continuous speech recognition experiments, the performance of the PLDA based approach improves over the principal component analysis (PCA) based approach and the two-dimensional PCA based approach for adaptation data longer than 12 s.

A Fast and Efficient Clutter Rejection Algorithm for SAR Imagery

A clutter rejection scheme is proposed for synthetic aperture radar (SAR) imagery based on two-stage two-dimensional principal component analysis (two-stage 2DPCA) followed by a bipolar eigenspace separation transformation (BEST) and a multilayer perceptron (MLP). For this, we have examined and analyzed four different algorithms. They are based on principal component analysis (PCA) both in one dimension (conventional PCA) and in two dimension with three different forms (2DPCA, alternative 2DPCA and two-stage 2DPCA), followed by BEST and MLP in each case. Feature extraction in different cases is carried out using respective PCA scheme. Each algorithm uses the BEST to further reduce dimensionality and enhance the generalization capability of the classifier. Classification between target chips and clutter chips is finally made through an MLP classifier. Experimental results on MSTAR public release database of SAR imagery are presented. Comparison of all the 2DPCA algorithms with an existing technique shows improvement both in performance and time. Moreover, all the 2DPCA algorithms compute eigenvectors and eigenvalues of image covariance matrix accurately and very efficiently with respect to time, and further reduces the effect of noise better than that in the case of PCA. Comparison reveals the fact that two-stage 2DPCA-based algorithm is the best (both in performance and time) between all algorithms.

Fault diagnosis for batch processes based on two-dimensional principal component analysis

Fault diagnosis for batch processes based on two-dimensional principal component analysis

SAR Images Recognition Combined Bidirectional 2DPCA with KPCA

The bidirectional 2DPCA (two-dimensional principal component analysis) method for SAR images recognition, can compress the columns and rows of images matrix and reduce the number of feature dimensions. However, it fails to use high order statistics information of image data, neglects the nonlinearity correlation between pixels. Therefore, this paper presents the method combined bidirectional 2DPCA with KPCA (Kernel Principal Component Analysis). This method not only compresses the dimensions of images data, but develops the superiority of KPCA in describing correlation between many pixels. Experimental results show that: this method can decrease calculated amount and raise recognition rate of SAR target effectively.

The investigation of the bacterial indicators and point sources of pollution for the Nanshih River, Taiwan: a case study

AbstractThe objective was to evaluate representative bacterial indicators found in the Nanshih River, a resource having multiple uses including recreation, water supply, and agriculture. Human activities were investigated in order to clarify the relationship between bacterial indicators and point source pollution discharged into this river. The optimal bioindicator was evaluated using four approaches by two-dimensional principal component analysis (PCA), which included ten water parameters, the median river pollution index parameters, bacterial numbers and specific fluorescence in situ hybridization indicators. The results indicate that Bifidobacterium spp. with the range of 1.80 ± 0.96–14.14 ± 1.24% were identified as the best bioindicator for the Nanshih River and these bacteria are able to identify four characteristic groups of point pollution sources using PCA. It is suggested the specific bacterial indicator need to be used for the regular monitoring of the Nanshih River in addition to the present re...

Ionospheric anomaly related to M=6.6, 26 August 2012, Tobelo earthquake near Indonesia: two-dimensional principal component analysis

This study employed two-dimensional Principal Component Analysis (2DPCA) and Principal Component Analysis (PCA) to examine the total electron content (TEC) in the ionosphere 5 days prior to the deep Tobelo earthquake. This event occurred at 15:05:37 on 26 August 2012 (UT) (M w =6.6) at a depth of 91.9 km near Indonesia. Using 2DPCA, a TEC precursor was identified near the epicenter of the earthquake lasting at least 10 minutes, from 06:10 to 06:20 on 24 August 2012 (UT). One possible cause for a TEC precursor of such short duration could be the high initial speed of released radon gas.

2DPCA with L1-norm for simultaneously robust and sparse modelling

Robust dimensionality reduction is an important issue in processing multivariate data. Two-dimensional principal component analysis based on L1-norm (2DPCA-L1) is a recently developed technique for robust dimensionality reduction in the image domain. The basis vectors of 2DPCA-L1, however, are still dense. It is beneficial to perform a sparse modelling for the image analysis. In this paper, we propose a new dimensionality reduction method, referred to as 2DPCA-L1 with sparsity (2DPCAL1-S), which effectively combines the robustness of 2DPCA-L1 and the sparsity-inducing lasso regularization. It is a sparse variant of 2DPCA-L1 for unsupervised learning. We elaborately design an iterative algorithm to compute the basis vectors of 2DPCAL1-S. The experiments on image data sets confirm the effectiveness of the proposed approach.

A Mixed Two-dimensional Linear Discriminate Method

Feature extraction is one of key technologies of the palmprint identification. In the light of the characteristics subspace palmprint identification technology, the two-dimensional principal component analysis, two-dimensional fisher linear discriminant and two-way two-dimensional principal component analysis algorithm is deeply analyzed. Based on two-dimensional subspace palmprint identification algorithm is a direct projection of the palmprint image matrix and is achieved very good results for dimension reduction. This paper proposed a mixed two-dimensional linear discriminant dimension reduction algorithm which can eliminate the relevance of rows and columns to get the best projection vector and extract optimal discriminant information. Experimental results show that the proposed method has faster extraction speed, higher recognition rate and better robustness. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2608

Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification

In recent years, verification based on thermal face images has been extensively studied because of its invariance to illumination and immunity to forgery. However, most of them have not given full consideration to high-verification performance and singular within-class scatter matrix problems. We propose a novel thermal face verification algorithm, which is named two-directional two-dimensional modified Fisher principal component analysis. First, two-dimensional principal component analysis (2-DPCA) is utilized to extract the optimal projective vector in the row direction. Then, 2-D modified Fisher linear discriminant analysis is implemented to overcome the singular within-class scatter matrix problem of the 2-DPCA space in the column direction. Comparative experiments on the natural visible and infrared facial expression thermal face subdatabase demonstrate that the proposed approach outperforms state-of-the-art methods in terms of verification performance.

Face recognition algorithm based on multi-level texture spectrum features and PCA

To improve the recognition rate of Principal Component Analysis(PCA) algorithm in face recognition,a new algorithm combining the image texture spectrum feature with PCA was proposed.Firstly,the texture unit operator was used to extract the texture spectrum feature of the face image.Secondly,PCA approach was used to reduce the dimensions of the texture spectrum feature.Finally,K-Nearest Neighbor(KNN) classification was chosen to recognize the face.ORL and Yale face database were used to test the proposed algorithm,and the recognition accuracies were 96.5% and 95% respectively,which were higher than those of PCA and Modular Two-Dimensional PCA(M2DPCA).The experimental results demonstrate the efficiency and accuracy of the proposed algorithm.

Theory of fractional covariance matrix and its applications in PCA and 2D-PCA

In this paper, according to the definition and applications of fractional moments, we give new definitions of the fractional variance and fractional covariance. Furthermore, we give the definition of fractional covariance matrix. Based on fractional covariance matrix, principal component analysis (PCA) and two-dimensional principal component analysis (2D-PCA), we propose two new techniques, called fractional principal component analysis (FPCA) and two-dimensional fractional principal component analysis (2D-FPCA), which extends PCA and 2D-PCA to fractional order form, and extends the transition recognition ranges of PCA and 2D-PCA. To evaluate the performances of FPCA and 2D-FPCA, a series of experiments are performed on two face image databases: ORL and Yale. Experiments show that two new techniques are superior to the standard PCA and 2D-PCA if choosing different order between 0 and 1.

Modified Local Directional Pattern 영상을 이용한 얼굴인식

일반적으로 이진패턴 변환은 조명 변화에 강인한 특성을 가지므로, 얼굴인식 및 표정인식 분야에 널리 사용되고 있다. 이에, 본 논문에서는 기존의 LDP(Local Directional Pattern)의 텍스처 성분을 개선한 MLDP(Modified LDP) 변환 영상에 2D-PCA(Two-Dimensional Principal Component Analysis) 알고리즘을 결합한 조명변화에 강인한 얼굴인식 방법에 대하여 제안한다. 기존의 LBP(Local Binary Pattern)나 LDP와 같은 이진패턴 변환들이 히스토그램 특징 추출을 위해 주로 사용되는 것과는 다르게, 본 논문에서 제안하는 방법은 MLDP 영상을 2D-PCA 특징추출을 위해 직접 사용한다는 특성을 갖는다. 제안 방법의 성능평가는 PCA(Principal Component Analysis), 2D-PCA 및 가버변환 영상과 LBP를 결합한 알고리즘을 사용하여, 다양한 조명변화 환경에서 구축된 Yale B 및 CMU-PIE 데이터베이스를 이용하여 수행되었다. 실험 결과, MLDP 영상과 2D-PCA를 사용한 제안 방법이 가장 우수한 인식 성능을 보임을 확인하였다. Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation

The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance (ANOVA) of the PCA scores between the experimental and simulation spike data. In the PIX condition, gi was found to decrease to approximately half its control value. CBX caused an approximately 30% decrease in gc from control levels. These results support the hypothesis that the glomeruli are control points for determining the spatiotemporal characteristics of olivocerebellar activity and thus may shape its ability to convey signals to the cerebellum that may be used for motor learning or motor control purposes.

A Novel Adaptive Fast Learning Algorithm for 2DPCA

We propose a novel adaptive fast learning (AFL) algorithm for two-dimensional principal component analysis (2DPCA) in this paper. As opposite to conventional PCA which is based on 1D data vectors, 2DPCA is based on 2D image matrices and thus has higher accuracy than conventional PCA when applied to applications such as face recognition, facial expression recognition, palmprint recognition, etc. Our proposed AFL algorithm simultaneously estimates both eigenvectors and corresponding eigenvalues, and then adaptively sets the learning rate parameters of neurons to ensure all neurons learning with almost the same fast speed. Requiring no image covariance matrix evaluation, the desired multiple eigenvectors of 2DPCA can thus be learned effectively in the form of weight vectors of neurons. The proposed AFL algorithm can also be applied to learning for T-2DPCA. Simulation experiments performed on face database such as the YaleB database clearly demonstrate that the proposed AFL algorithm performs very well and thus is a very effective computational tool for both 2DPCA and T-2DPCA.

Block-wise two-directional 2DPCA with ensemble learning for face recognition

Two-Dimensional Principal Component Analysis (2DPCA) is a well-known feature extraction method for face recognition. One of the main drawbacks of this method, in comparison with the vector-based PCA, is that it needs many more coefficients to represent the feature matrix of an image. Two-Directional 2DPCA ((2D)2PCA), proposed in the literature, attempts to alleviate this problem. However, it fails to improve the recognition accuracy of 2DPCA. In addition, (2D)2PCA follows a global feature extraction approach that might fail to preserve some important local features. In this paper, we propose Block-Wise (2D)2PCA to enhance the performance of (2D)2PCA by preserving the local informative variations. On average, the feature matrices produced by the proposed method and those formed by (2D)2PCA are about the same size. However, our experiments on four face recognition databases indicate that our method is superior to (2D)2PCA in terms of the recognition accuracy.