• Journal of Korea Multimedia Society
• /
• v.17 no.12
• /
• pp.1446-1452
• /
• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• ETRI Journal
• /
• v.31 no.4
• /
• pp.438-447
• /
• 2009

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or $(2D)^2$LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted $(2D)^2$LDA, or RW$(2D)^2$LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW$(2D)^2$LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

• Journal of Korea Multimedia Society
• /
• v.13 no.6
• /
• pp.817-824
• /
• 2010

Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.12C
• /
• pp.1162-1166
• /
• 2005

A new low dimensional feature representation technique is presented in this paper. Linear discriminant analysis is a popular feature extraction method. However, in the case of high dimensional data, the computational difficulty and the small sample size problem are often encountered. In order to solve these problems, we propose two dimensional direct LDA algorithm, which directly extracts the image scatter matrix from 2D image and uses Direct LDA algorithm for face recognition. The ORL face database is used to evaluate the performance of the proposed method. The experimental results indicate that the performance of the proposed method is superior to DLDA.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2991-3007
• /
• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.35-42
• /
• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.

• Proceeding of EDISON Challenge
• /
• 2015.03a
• /
• pp.400-404
• /
• 2015

2차원 물질인 metal mono chalcogenides(MMC) 중 GaS와 GaSe를 대상으로 하여 층과 층 사이의 van der Waals(vdW) 상호작용을 density functional theory(DFT) 계산을 이용해 연구하였다. Local density approximation(LDA)와 generalized gradient approximation (GGA)의 두 가지 다른 exchange correlation functional을 이용하고, 또한 두 개의 층 사이에 작용하는 van der Waals 상호작용을 고려한 LDA-D2, GGA-D2 계산을 수행하였다. 이와 같은 네 가지 방법으로 층간거리를 바꾸어 binding energy curve를 계산하였다. 그 결과 GGA-D2계산이 MMC의 층간 상호 작용을 가장 잘 기술하였다.

• Journal of the Korean Magnetics Society
• /
• v.16 no.2
• /
• pp.115-120
• /
• 2006

Electronic and magnetic structure of $Cr_2Te_3$ have been studied, which is a material with complex magnetic structure. Density of states and magnetic moments show better agreement with experiments than LDA if they are obtained with the correlation effect of Cr-d electrons taken into account by the LDA+U method. In these calculations, the magnitude of the correlation effect is found to be 1.7 eV. It is shown that the magnitude of experimental magnetic moments of Cr atoms can be explained if the ferromagnetic states and the ferrimagnetic states have the same energy to be degenerate.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.5
• /
• pp.755-765
• /
• 2015

This paper is concerned with Interval Type-2 Radial Basis Function Neural Network classifier realized with the aid of Census Transform(CT) and (2D)2LDA methods. CT is considered to improve performance of face recognition in a variety of illumination variations. (2D)2LDA is applied to transform high dimensional image into low-dimensional image which is used as input data to the proposed pattern classifier. Receptive fields in hidden layer are formed as interval type-2 membership function. We use the coefficients of linear polynomial function as the connection weights of the proposed networks, and the coefficients and their ensuing spreads are learned through Conjugate Gradient Method(CGM). Moreover, the parameters such as fuzzification coefficient and the number of input variables are optimized by Artificial Bee Colony(ABC). In order to evaluate the performance of the proposed classifier, Yale B dataset which consists of images obtained under diverse state of illumination environment is applied. We show that the results of the proposed model have much more superb performance and robust characteristic than those reported in the previous studies.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.32 no.5
• /
• pp.399-402
• /
• 2019

The electromagnetic properties of heavy fermion $CePd_2Si_2$ are investigated using density functional theory using the local density approximation (LDA) and LDA+U methods. The Ce f-bands are located near the Fermi energy and hybridized with the Pd-3d states. This hybridization plays an important role in generating the physical characteristics of this compound. The magnetic moment of $CePd_2Si_2$ calculated within the LDA scheme does not match with the experimental result because of the strong correlation interaction between the f orbitals. The calculation shows that the specific heat coefficient underestimates the experimental value by a factor of 5.98. This discrepancy is attributed to the formation of quasiparticles. The exchange interaction between the local f electrons and the conduction d electrons is the reason for the formation of quasiparticles. The exchange interaction is significant in $CePd_2Si_2$, which makes the quasiparticle mass increase. This enhances the specific heat coefficient.