Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set

In this paper, a reformative scatter difference discriminant criterion (SDDC) with fuzzy set theory is studied. The scatter difference between between-class and within-class as discriminant criterion is effective to overcome the singularity problem of the within-class scatter matrix due to small sample size problem occurred in classical Fisher discriminant analysis. However, the conventional SDDC assumes the same level of relevance of each sample to the corresponding class. So, a fuzzy maximum scatter difference analysis (FMSDA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution information of original samples, and this information is utilized to redefine corresponding scatter matrices which are different to the conventional SDDC and effective to extract discriminative features from overlapping (outlier) samples. Experiments conducted on FERET face databases demonstrate the effectiveness of the proposed method.

This paper proposes a new method of face image feature extraction, namely, the fuzzy 2DLDA (F2DLDA) based on the 2D fisher discriminant criterion and fuzzy set theory. In the proposed method, we calculate membership degree matrix by FKNN, then we incorporate the membership degree into the definition of the between-class scatter matrix and within-class scatter matrix and get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experiments on the ORL and FERET face databases show that the new method can work well.

Fault detection based on robust characteristic dimensionality reduction

Feature extraction based on fuzzy 2DLDA

Fuzzy MSD based feature extraction method for face recognition

For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly diagonalizing two scatter matrices, ICS is designed to find structure in the data while providing affine invariant components. Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. However, ICS has garnered minimal attention within the context of clustering. Two challenges associated with ICS include choosing the pair of scatter matrices and selecting the components to retain. For effective clustering purposes, it is demonstrated that the best scatter pairs consist of one scatter matrix capturing the within-cluster structure and another capturing the global structure. For the former, local shape or pairwise scatters are of great interest, as is the minimum covariance determinant (MCD) estimator based on a carefully chosen subset size that is smaller than usual. The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. In an extensive simulation study and empirical applications with benchmark data sets, various combinations of scatter matrices as well as component selection criteria are compared in situations with and without outliers. Overall, the new approach of tandem clustering with ICS shows promising results and clearly outperforms the PCA-based approach.

The goal of this paper is to present a new fuzzy logic-based feature extraction method, called Fuzzy Generalized Two-Dimensional Fisher's Linear Discriminant (FG-2DFLD) method for face recognition. More specifically, the FG-2DFLD method is an extension of the G-2DFLD method. In FG-2DFLD method, we introduced fuzzy membership values to all the training samples and incorporated them into the within-class and between-class scatter matrices along the row and column directions. The class-wise mean images are also generated by considering the membership values. The fuzzy k-nearest neighbour (FKNN) algorithm is applied for generating the corresponding degrees of class membership. Finally, the feature matrix generated by solving the eigenvalue problem of these scatter matrices actually provide better discrimination information as supported by the simulation results on the AT&T (formally known as ORL) and UMIST face databases using a radial basis function (RBF) neural network.

The problem of ultra-high dimensionality small samples exists in the face data. In order to obtain discriminative features with higher generalization ability and improve the face recognition rate, a weighted maximum margin criterion (WMMC) method is presented for extracting discriminative features of face data. The method considers that the actual inter-class scatter is less than the inter-class scatter reflected by the inter-class scatter matrix calculated with limited training samples, and the actual intra-class scatter is more than the intra-class scatter reflected by the intra-class scatter matrix calculated with limited training samples, the WMMC function is obtained by recalculating the inter-class scatter matrix and intra-class scatter matrix. Experimental results using minimum distance (MD) and k-nearest neighbor (kNN) classifiers on ORL and Yale face datasets show that WMMC extracts discriminative features with higher generalization ability and significantly improves face recognition rate compared to maximum margin criterion (MMC).KeywordsFeature extractionWeighted maximum margin criterion (WMMC)Linear discriminant analysis (LDA)Face recognition

In order to improve the recognition and detection ability of movie and TV 3D animation images, a method of movie and TV 3D animation image information fusion and feature extraction based on dynamic information fusion and edge contour feature detection is proposed. Establishing a visual information acquisition model of a movie and television three-dimensional animation image, performing corner detection and feature point extraction of the movie and television three-dimensional animation image by adopting a key feature point matching method, extracting dynamic information feature quantity of the edge of the movie and television three-dimensional animation image, combining saliency detection, feature fusion and optimal segmentation of the movie and television three-dimensional animation image, establishing a super-resolution reconstruction feature distribution set of the movie and television three-dimensional animation image. The edge information space region fusion method is used to fuse pixel information and optimize feature reorganization of movie and television three-dimensional animation images. The fuzzy feature distribution set of movie and television three-dimensional animation images is extracted. The information fusion and feature extraction of movie and television three-dimensional animation images are realized by combining wavelet multi-layer decomposition algorithm. The simulation results show that the feature resolution capability of using this method for information fusion and feature extraction of movie and TV 3D animation images is higher, and the output peak signal-to-noise ratio of movie and TV 3D animation image reconstruction is improved.

This paper gives a new method for image feature extraction named “Fuzzy Block Truncation Coding”. Performance of Fuzzy Block Truncation Coding for classification with different methods of pre-processing, in various color spaces is analyzed. Fuzzy Block Truncation Coding is used to extract features from images in different color spaces such as RGB, YUV, YCrCb, HSV and LUV. Different classifiers such as C4.5, Naive Bayes and Random Forest with different pre-processing methods such as Fuzzy and PKIDiscretize are used. Experimental results obtain using standard dataset proves that proposed feature extraction scheme works better image classification.

A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter matrices. Experimental results obtained on the FERET database demonstrate that the proposed method improves the classification rate performance.

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Feature extraction is an important step before actual learning. Although many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. This paper proposes a novel feature extraction method, called orientation distance–based discriminative (ODD) feature extraction, particularly designed for multi-class classification problems. Our proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine an appropriate kernel function and map the input data with all classes into a feature space where the classes of the data are well separated. In the second step, we put forward two variants of ODD features, i.e., one-vs-all-based ODD and one-vs-one-based ODD features. We first construct hyper-plane (SVM) based on one-vs-all scheme or one-vs-one scheme in the feature space; we then extract one-vs-all-based or one-vs-one-based ODD features between a sample and each hyper-plane. These newly extracted ODD features are treated as the representative features and are thereafter used in the subsequent classification phase. Extensive experiments have been conducted to investigate the performance of one-vs-all-based and one-vs-one-based ODD features for multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods.

In this paper, we propose a manifold geometry based projective nonnegative matrix factorization linear dimensionality reduction method, called neighborhood preserving orthogonal projective nonnegative matrix factorization (NPOPNMF), for feature extraction of hyperspectral image. By adding constraints on projective nonnegative matrix factorization (PNMF) that each data point can be represented as a linear combination of its neighbors, NPOPNMF preserves local neighborhood geometrical structure of hyperspectral data in the reduced space, and overcomes the Euclidean limitation of PNMF. The metric structure of original high-dimensional hyperspectral data space is preserved due to the orthogonality of projection matrix. NPOPNMF can be performed in either supervised or unsupervised mode according to the construction of adjacency graph and it can improve the discriminant performance of PNMF. Theoretical analysis and experimental results on hyperspectral data sets demonstrate that the proposed method is an effective and promising method for hyperspectral image feature extraction.