• 한국멀티미디어학회논문지
• /
• 제17권12호
• /
• 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
• /
• 제31권4호
• /
• 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%.

• 한국멀티미디어학회논문지
• /
• 제13권6호
• /
• 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.

• 한국통신학회논문지
• /
• 제30권12C호
• /
• pp.1162-1166
• /
• 2005

본 논문에서는 얼굴 인식을 위한 새로운 저차원 특징 표현 기법을 제안하였다. 선형판별기법(LDA)는 인기있는 특징추출 기법이다. 하지만 고차원 데이터의 경우에 계산적인 복잡도가 높고 샘플의 개수가 적은 경우 역행렬을 구할 수 없는 특이행렬문제에 직면한다. 이러한 문제들을 해결하기 위해 일반적인 선형판별기법과 다르게 우리는 이차원 이미지 공분산 행렬을 구한 다음 직접선형판별기법(dirct LDA)을 적용하였으며 이것을 2D-DLDA라고 부른다. ORL 얼굴데이터베이스를 사용하여 실험한 결과 기존의 직접선형판별기법보다 성능이 우수함을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권9호
• /
• 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.

• 한국컴퓨터정보학회논문지
• /
• 제19권10호
• /
• pp.35-42
• /
• 2014

주어진 실내 환경에 위치한 여러 장애물에 대한 정보를 사전에 훈련하고 인식하여 로봇의 인지 능력을 향상시키기 위해 스테레오비전 센서의 RGB-D 이미지에서 인텐시티를 기반으로 일정 거리 안에 있는 장애물을 검출하는 기법을 제시한다. RGB-D 인텐시티 정보에 대해 PCA, ICA, LDA, SVM의 주요 패턴인식 알고리즘을 적용하여 인식률 및 실행시간을 구하고, 여러 패턴인식 알고리즘 중에서 어떤 알고리즘이 인식률 및 실행시간 측면에서 적용이 가능한지를 제시한다. 실험결과, RGB-D 데이터와 인텐시티 데이터를 비교한 결과 정확도면에서는 RGB-D 데이터가 4.2% 높은 인식률을 보였으나 훈련시간은 인텐시티 데이터가 RGB-D 이미지에 비해 LDA의 경우 29%, SVM의 경우 31% 빠르게 처리되었으며 테스트시간은 LDA의 경우 70%, SVM의 경우 33% 빠르게 처리되어 모바일 로봇 장애물 인식에 인텐시티 데이터를 사용하는 것이 정확도면에서도 우수하고 처리 속도 면에서 높은 개선효과가 있다.

• EDISON SW 활용 경진대회 논문집
• /
• 제4회(2015년)
• /
• 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.

• 한국자기학회지
• /
• 제16권2호
• /
• pp.115-120
• /
• 2006

자기구조가 복잡한 것으로 알려져 있는 $Cr_2Te_3$의 전자구조와 자기구조를 이론적 방법으로 연구하였다. LDA+U 방법에 의해 Cr-d 전자의 상관효과를 고려해서 계산하면 전자의 상태밀도와 자기모멘트가 국소밀도근사(LDA)에 의한 것보다 실험과 더 잘 일치하는 것을 보였다. 이 과정에서 $Cr_2Te_3$에서 Cr-d 전자의 상관효과의 크기 U=1.7 eV를 구했다. $Cr_2Te_3$의 강자성 상태와 준강자성 상태의 에너지가 같아서 축퇴된다면 실험에서 알려진 Cr의 자기모멘트를 설명할 수 있음을 보였다.

• 전기학회논문지
• /
• 제64권5호
• /
• 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.

• 한국전기전자재료학회논문지
• /
• 제32권5호
• /
• 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.