Improving feature extraction by replacing the Fisher criterion by an upper error bound

A lot of alternatives and constraints have been proposed in order to improve the Fisher criterion. But most of them are not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound for the error rate a criterion is developed which can improve the classification performance.     

Unsupervised feature extraction via kernel subspace techniques

This paper provides a new insight into unsupervised feature extraction techniques based on kernel subspace models. The data projected onto kernel subspace models are new data representations which might be better suited for classification. The kernel subspace models are always described exploiting the dual form for the basis vectors which requires that the training data must be available even during the test phase. By exploiting an incomplete Cholesky decomposition of the kernel matrix, a computationally less demanding implementation is proposed. Online benchmark data sets allow the evaluation of these feature extraction methods comparing the performance of two classifiers which both have as input either the raw data or the new representations.     

Hierarchical palmprint identification via multiple feature extraction

Biometric computing offers an effective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.     

A fast method of feature extraction for kernel MSE

In this paper, a fast method of selecting features for kernel minimum squared error (KMSE) is proposed to mitigate the computational burden in the case where the size of the training patterns is large. Compared with other existent algorithms of selecting features for KMSE, this iterative KMSE, viz. IKMSE, shows better property of enhancing the computational efficiency without sacrificing the generalization performance. Experimental reports on the benchmark data sets, nonlinear autoregressive model and real problem address the efficacy and feasibility of the proposed IKMSE. In addition, IKMSE can be easily extended to classification fields.     

Generalized K-L transform based combined feature extraction

In this paper, we combine two kinds of features together by virtue of complex vectors and then use the developed generalized K-L transform (or expansion) for feature extraction. The experiments on NUST603 handwritten Chinese character database and CENPARMI handwritten digit database indicate that the proposed method can improve the recognition rate significantly.     

一种可最优化计算特征规模的互信息特征提取

利用矩阵特征向量分解，提出一种可最优化计算特征规模的互信息特征提取方法．首先，论述了高斯分布假设下的该互信息判据的类可分特性，并证明了现有典型算法都是本算法的特例；然后，在给出该互信息判据严格的数学意义基础上，提出了基于矩阵特征向量分解计算最优化特征规模算法；最后，通过实际数据验证了该方法的有效性     

Local structure based supervised feature extraction

In the past few years, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel feature extraction method, called locally discriminating projection (LDP). LDP utilizes class information to guide the procedure of feature extraction. In LDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and the class information. The similarity has several good properties which help to discover the true intrinsic structure of the data, and make LDP a robust technique for the classification tasks. We compare the proposed LDP approach with LPP, as well as other feature extraction methods, such as PCA and LDA, on the public available data sets, FERET and AR. Experimental results suggest that LDP provides a better representation of the class information and achieves much higher recognition accuracies.     

An MRF-based kernel method for nonlinear feature extraction

Nonlinear kernel-based feature extraction algorithms have recently been proposed to alleviate the loss of class discrimination after feature extraction. When considering image classification, a kernel function may not be sufficiently effective if it depends only on an information resource from the Euclidean distance in the original feature space. This study presents an extended radial basis kernel function that integrates multiple discriminative information resources, including the Euclidean distance, spatial context, and class membership. The concepts related to Markov random fields (MRFs) are exploited to model the spatial context information existing in the image. Mutual closeness in class membership is defined as a similarity measure with respect to classification. Any dissimilarity from the additional information resources will improve the discrimination between two samples that are only a short Euclidean distance apart in the feature space. The proposed kernel function is used for feature extraction through linear discriminant analysis (LDA) and principal component analysis (PCA). Experiments with synthetic and natural images show the effectiveness of the proposed kernel function with application to image classification.     

Automatic head and facial feature extraction based on geometry variations

Facial anthropometry plays an important role in ergonomic applications. Most ergonomically designed products depend on stable and accurate human body measurement data. Our research automatically identifies human facial features based on three-dimensional geometric relationships, revealing a total of 67 feature points and 24 feature lines — more than the definitions associated with MPEG-4. In this study, we also verify the replicability, robustness, and accuracy of this feature set. Even with a lower-density point cloud from a non-dedicated head scanner, this method can provide robust results, with 86.6% validity in the 5 mm range. We also analyze the main 31 feature points on the human face, with 96.7% validity of less than 5 mm.     

On the feature extraction in discrete space

In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these orderings as the new attributes. Our simulation results on 15 datasets from UCI repository [3] show that the novel classifiers perform better than the proper ones in terms of error rate and complexity.     

A multi-manifold discriminant analysis method for image feature extraction

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance.     

Sparse two-dimensional local discriminant projections for feature extraction

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) and two-dimensional discriminant locality preserving projections (2DDLPP) were recently proposed to directly extract features form 2D face matrices to improve the performance of two-dimensional locality preserving projections (2DLPP). But all of them require a high computational cost and the learned transform matrices lack intuitive and semantic interpretations. In this paper, we propose a novel method called sparse two-dimensional locality discriminant projections (S2DLDP), which is a sparse extension of graph-based image feature extraction method. S2DLDP combines the spectral analysis and L₁-norm regression using the Elastic Net to learn the sparse projections. Differing from the existing 2D methods such as 2DLPP, 2DDLP and 2DLGEDA, S2DLDP can learn the sparse 2D face profile subspaces (also called sparsefaces), which give an intuitive, semantic and interpretable feature subspace for face representation. We point out that using S2DLDP for face feature extraction is, in essence, to project the 2D face images on the semantic face profile subspaces, on which face recognition is also performed. Experiments on Yale, ORL and AR face databases show the efficiency and effectiveness of S2DLDP.     

一种基于特征提取方法的智能预测算法

针对色织生产调度过程中的一类整经轴数预测问题,提出一种整经轴数智能预测算法．首先基于线性特征提取方法（PCA）和非线性特征提取方法（LLE）对影响整经轴数的多维属性参数进行特征提取;然后采用前向神经网络进行整经轴数预测．数值计算结果表明,所提出的方法能满足实际生产过程整经轴数预测的需要．     

Palmprint feature extraction using 2-D Gabor filters

Biometric identification is an emerging technology that can solve security problems in our networked society. A few years ago, a new branch of biometric technology, palmprint authentication, was proposed (Pattern Recognition 32(4) (1999) 691) whereby lines and points are extracted from palms for personal identification. In this paper, we consider the palmprint as a piece of texture and apply texture-based feature extraction techniques to palmprint authentication. A 2-D Gabor filter is used to obtain texture information and two palmprint images are compared in terms of their hamming distance. The experimental results illustrate the effectiveness of our method.     

Stochastic neighbor projection on manifold for feature extraction

This paper develops a manifold-oriented stochastic neighbor projection (MSNP) technique for feature extraction. MSNP is designed to find a linear projection for the purpose of capturing the underlying pattern structure of observations that actually lie on a nonlinear manifold. In MSNP, the similarity information of observations is encoded with stochastic neighbor distribution based on geodesic distance metric, then the same distribution is required to be hold in feature space. This learning criterion not only empowers MSNP to extract nonlinear feature through a linear projection, but makes MSNP competitive as well by reason that distribution preservation is more workable and flexible than rigid distance preservation. MSNP is evaluated in three applications: data visualization for faces image, face recognition and palmprint recognition. Experimental results on several benchmark databases suggest that the proposed MSNP provides a unsupervised feature extraction approach with powerful pattern revealing capability for complex manifold data.     

Human facial feature extraction for face interpretation and recognition

Facial features' extraction algorithms which can be used for automated visual interpretation and recognition of human faces are presented. Here, we can capture the contours of the eye and mouth by a deformable template model because of their analytically describable shapes. However, the shapes of the eyebrow, nostril and face are difficult to model using a deformable template. We extract them by using an active contour model (snake). In the experiments, 12 models are photographed, and the feature contours are extracted for each portrait.     

A collaborative representation based projections method for feature extraction

In graph embedding based methods, we usually need to manually choose the nearest neighbors and then compute the edge weights using the nearest neighbors via L2 norm (e.g. LLE). It is difficult and unstable to manually choose the nearest neighbors in high dimensional space. So how to automatically construct a graph is very important. In this paper, first, we give a L2-graph like L1-graph. L2-graph calculates the edge weights using the total samples, avoiding manually choosing the nearest neighbors; second, a L2-graph based feature extraction method is presented, called collaborative representation based projections (CRP). Like SPP, CRP aims to preserve the collaborative representation based reconstruction relationship of data. CRP utilizes a L2 norm graph to characterize the local compactness information. CRP maximizes the ratio between the total separability information and the local compactness information to seek the optimal projection matrix. CRP is much faster than SPP since CRP calculates the objective function with L2 norm while SPP calculate the objective function with L1 norm. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that CRP works well in feature extraction and leads to a good recognition performance.     

Prediction-based geometric feature extraction for 2D laser scanner

This paper presents a novel algorithm for detecting line and circle features from 2D laser range scans. Unlike the conventional methods that use two stages for separating the features: data segmentation and feature separation in each segment, the proposed algorithm adopts a new structure and thus the computation complexity is much reduced. Moreover, it does not depend on prior knowledge of the environment, and it requires a minimum number of points per segment. We utilize prediction to achieve the above goals, so the algorithm is named prediction-based feature extraction (PFE). The efficiency and accuracy of the method is demonstrated by the experiments results.     

Adaptive S transform for feature extraction in voltage sags

This paper proposes an adaptive S transform (AST) to extract the feature vectors of voltage sags. With the effective window width matches the Fourier spectrum of sag signals, the standard deviation $\sigma$ of Gaussian window may be determined as well. The narrowest and the widest window width of AST are obtained without additional parameters and iterative computing. Then, the optimal frequency resolution and time resolution are got respectively. Compared with ST, AST provides better time–frequency resolution to extract more precise feature vectors of eight types of voltage sags. Based on the time–frequencyrepresentation of AST, five disturbance features are extracted to construct the feature vector in this paper. In addition, four machine learning classifiers and two fuzzy clustering classifiers are used to analyze the validity and redundancy of these features. Through analyzing the classification accuracies and time costs of these classifiers with different training sets and different level of noise, it can be concluded that the machine learning classifiers perform better in classification accuracy and stability than fuzzy clustering classifiers.