Efficient Feature Extraction Method Research Articles

Feature extraction using local structure preserving discriminant analysis

In this paper, an efficient feature extraction method, named local structure preserving discriminant analysis (LSPDA), is presented. LSPDA constructs the local scatter and the between-class scatter to characterize the sub- and multi-manifold information respectively. More specifically, the local structure 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 based on a parameter-free neighborhood decision rule, and the between-class structure is constructed according to the importance degrees of the not-same-class points measured by a strictly monotonically decreasing function. After the local scatter and the between-class scatter have been characterized, the novel feature extraction criterion is derived via maximizing the difference between the between-class scatter and the local scatter. Experimental results on the Wine dataset, AR, FERET, CMU PIE, ORL and LFW face databases show the effectiveness of the proposed method.

Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture features

Content-based image retrieval (CBIR) systems provide a potential solutions of retrieving semantically similar images from large image repositories against any query image. The research community is competing for more efficient and effective methods of content-based image retrieval, so they can be employed in serving time critical applications in scientific and industrial domains. In this paper, we have combined genetic algorithm and support vector machines to reduce the existing gap between high-level semantic content of the images and the information provided by their low-level descriptors. To maximize the performance of proposed technique, an efficient feature extraction method is introduced, which is based on the concept of in-depth texture analysis. To further enhance the capabilities of proposed method, we employed a way through which the risk of mis-associations can be avoided. To justify the effectiveness of the proposed method, we compared it against several popular CBIR techniques and show a significant improvement in terms of accuracy and stability based on Corel image gallery.

Fast and Accurate Home Photo Categorization for Handheld Devices using MPEG-7 Descriptors

Home photo categorization has become an issue for practical use of photos taken with various devices. But it is a difficult task because of the semantic gap between physical images and human perception. Moreover, the object-based learning for overcoming this gap is hard to apply to handheld devices due to its computational overhead. We present an efficient image feature extraction method based on MPEG-7 descriptors and a learning structure constructed with multiple layers of Support Vector Machines for fast and accurate categorization of home photos. Experiments on diverse home photos demonstrate outstanding performance of our approach in terms of the categorization accuracy and the computational overhead.

Locality Preserving Maximum Scatter Difference Projection for Face Recognition

Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.

Computer Aided Technique for Finger Print Image Enhancement and Minutiae Extraction

Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, and unacceptable error rates. To represent the large amount of data in the biometric images an efficient feature extraction method is needed. This paper presents the feature extraction of fingerprint image processing stages such as image Preprocessing, Converting the image in to gray scale, Image Enhancement can be performed with the help of Discrete Fourier transformation, image Binarization, image segmentation, image thinning, Minutiae Extraction after Minutiae Extraction again segmentation technique is used .In finger print images there are foreground regions and background regions where foreground region show the ridges and valleys while the background regions are to be left out. .The foreground regions have high value while the background regions have low values. Segmentation separates the foreground region from the background image for the reliable extraction of minutiae. The feature extraction is the first step used for matching used in biometric recognition. After feature extraction the next step is preprocessing of Minutiae. The extracted feature is called as templates, which is used in matching.

Emotion recognition from facial EMG signals using higher order statistics and principal component analysis

Higher order statistics (HOS) is an efficient feature extraction method used in diverse applications such as bio signal processing, seismic data processing, image processing, sonar, and radar. In this work, we have investigated the application of HOS to derive a set of features from facial electromyography (fEMG) signals for classifying six emotional states (happy, sad, afraid, surprised, disgusted, and neutral). fEMG signals were collected from different types of subjects in a controlled environment using audio-visual (film clips/ video clips) stimuli. Acquired fEMG signals were preprocessed using moving average filter and a set of statistical features were extracted from fEMG signals. Extracted features were mapped into corresponding emotions using k-nearest neighbor classifier. Principal component analysis was utilized to analyze the efficacy of HOS features over conventional statistical features on retaining the emotional information retrieval from fEMG signals. The results of this work indicate an improved mean emotion recognition rate of 69.5% from this proposed methodology to recognize six emotional states.

Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

Mixed PCA and Wavelet Transform based Effective Feature Extraction for Efficient Tumor Classification using DNA Microarray Gene Expression Data

Cancer classification is an emerging research area in the field of bioinformatics. Gene expression profiles using microarray data play important role in accurate tumor diagnosis. Hence correlation between gene expression profiles to disease through microarray data and its analysis has been an intensive task in molecular biology. As the microarray data have thousands of genes and very few samples, it is crucial to develop techniques to effectively exploit the huge quantity of data produced. Thus efficient feature extraction and computational method development is indispensible for the analysis. In this paper a mixed feature extraction method by combining principal component analysis (PCA) and discrete wavelet transform (DWT) has been proposed to detect informative genes effectively. The PCA is a dimensionality reduction algorithm which aims to map high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Further a feature extraction method based on the DWT is proposed. The approximation coefficients obtained by the decomposition at a particular level is used as the features for further study. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification with low computational complexity and high accuracy.

Feature extraction through contourlet subband clustering for texture classification

Feature extraction is an important processing procedure in texture classification. For feature extraction in the wavelet domain, the energies of subbands are usually extracted for texture classification. However, the energy of one subband is just a specific feature. In this paper, we propose an efficient feature extraction method for texture classification. In particular, feature vectors are obtained by c-means clustering on the contourlet domain as well as using two conventionally extracted features that represent the dispersion degree of contourlet subband coefficients. The c-means clustering algorithm is initialized via a nonrandom initialization scheme. By investigating these feature vectors, we employ a weighted L1-distance for comparing any two feature vectors that represent the corresponding subbands of two images and define a new distance between two images. According to the new distance, a k-Nearest Neighbor (kNN) classifier is utilized to perform texture classification, and experimental results show that our proposed approach outperforms five current state-of-the-art texture classification approaches.

Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data

Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA) with discrete wavelet transform (DWT) to detect informative genes. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.

Target detection of hyperspectral images based on their Fourier spectral features

Original spectral features contain information pertinent to certain target spectral features. Without an efficient spectral feature extraction method, the target detection performance might be degraded. We present spectral feature extraction techniques based on the Fourier domain for use in target detection. These feature extraction methods are the Fourier magnitude (FM), Fourier phase (FP), and Fourier coefficient selection (FCS) methods. In our target detection experiments, we compared the proposed methods to the principle component analysis (PCA) and independent component analysis (ICA) methods and the original spectral features. The experiment results show that the FCS target detection accuracy is 95.75%, whereas the accuracies of the FM, FP, PCA, ICA methods, and the original spectral features are 86.76%, 36.28%, 84.51%, 74.49%, and 78.92%, respectively. The average feature extraction times of the proposed methods are 223% faster than that found for the PCA and 304% faster than the ICA methods.

Dimension Reduction by Mutual Information Discriminant Analysis

In the past few decades, researchers have proposed many discriminant analysis (DA) algorithms for the study of high-dimensional data in a variety of problems. Most DA algorithms for feature extraction are based on transformations that simultaneously maximize the between-class scatter and minimize the withinclass scatter matrices. This paper presents a novel DA algorithm for feature extraction using mutual information (MI). However, it is not always easy to obtain an accurate estimation for high-dimensional MI. In this paper, we propose an efficient method for feature extraction that is based on one-dimensional MI estimations. We will refer to this algorithm as mutual information discriminant analysis (MIDA). The performance of this proposed method was evaluated using UCI databases. The results indicate that MIDA provides robust performance over different data sets with different characteristics and that MIDA always performs better than, or at least comparable to, the best performing algorithms.

Illumination Invariant Face Recognition Using the Statistical Features of BDIP and Wavelet Transform

In this paper, an efficient feature extraction method based on local statistics features of block difference of inverse probabilities (BDIP) and the wavelet transform is proposed for face recognition. In the proposed method, the BDIPs are first computed in a face in order to overcome the variation of illumination and facial expressions. The obtained BDIP image is then decomposed into wavelet subbands. In order to reduce the dimensionality of the feature vector, each BDIP subband is partitioned into a set of blocks. The means and variances are then calculated from all the blocks in each subband and are fused into a feature vector. Experimental results on ORL and FERET databases show that the proposed method achieves higher recognition accuracies than the wavelet-based methods with higher dimensionality reduction of the feature vector. It also outperforms the other well known methods such as PCA and the DCT with the zigzag scanning. Index Terms—Face recognition (FR), discrete wavelet transforms (DWT), wavelet packet decomposition (WPD), block difference of inverse probabilities (BDIP), support vector machine (SVM).

A Face Recognition System Based on BDIP and DCT Pyramid

In this paper, an efficient feature extraction method based on block difference of inverse probabilities (BDIP) and DCT pyramid for face recognition is proposed. The BDIP is first computed in a face image in order to overcome the variation of illumination. The extracted BDIP image is then decomposed using DCT pyramid. The DCT pyramid decomposes the BDIP image into an approximation subband and a set of reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods and the other well known methods such as the PCA and the block-based DCT with the zigzag scanning.

An Efficient Feature Extraction Method, Global Between Maximum and Local Within Minimum, and Its Applications

Feature extraction plays an important role in preprocessing procedure in dealing with small sample size problems. Considering the fact that LDA, LPP, and many other existing methods are confined to one case of the data set. To solve this problem, we propose an efficient method in this paper, named global between maximum and local within minimum. It not only considers the global structure of the data set, but also makes the best of the local geometry of the data set through dividing the data set into four domains. This method preserves relations of the nearest neighborhood, as well as demonstrates an excellent performance in classification. Superiority of the proposed method in this paper is manifested in many experiments on data visualization, face representative, and face recognition.

Efficient fractal dimension calculation method for feature extraction of skin images

We applied the method of power spectrum to evaluate the state of the human skin using the analysis of textured images by fractal dimension. We extracted features from these skin images to classify the age and gender. For a good estimation, achieving pictures that include accurate information among taken pictures is necessary. However, in practical use the time required to find pictures that include accurate information is a problem. This paper presents an efficient method to calculate accurate fractal dimensions by extracting only relevant information from a picture. The fractal dimension is calculated continuously after cropping sections of a picture to include only the accurate information.

An Efficient Method for Face Feature Extraction and Recognition based on Contourlet Transform and Principal Component Analysis using Neural Network

this paper, an efficient face recognition method based on discrete Contourlet transform using PCA and Neural Network classifier is proposed. Each face from the Face Dataset is decomposed using the Discrete Contourlet transform. The Contourlet coefficients of low frequency & high frequency in different scales & various angles are obtained. The frequency coefficients are used as a feature vector for further process. The PCA (Principal component analysis) is used to reduce the dimensionality of the feature vector. The reduced feature vector is used for learning phase of Neural Network classifier. The test databases are projected on Contourlet-PCA subspace to retrieve reduced coefficients. These coefficients are used to match the feature vector coefficients of training dataset using Neural Network Classifier and the results are compared with Euclidean Distance Classifier. The experiments are carried out using Face94 and IIT_Kanpur database.

Local appearance based face recognition method using block based steerable pyramid transform

In this paper, an efficient local appearance feature extraction method based on Steerable Pyramid (S-P) wavelet transform is proposed for face recognition. Local information is extracted by computing the statistics of each sub-block obtained by dividing S-P sub-bands. The obtained local features of each sub-band are combined at the feature and decision level to enhance face recognition performance. The purpose of this paper is to explore the usefulness of S-P as feature extraction method for face recognition. The proposed approach is compared with some related feature extraction methods such as principal component analysis (PCA), as well as linear discriminant analysis LDA and boosted LDA. Different multi-resolution transforms, wavelet (DWT), gabor, curvelet and contourlet, are also compared against the block-based S-P method. Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.

Reformative nonlinear feature extraction using kernel MSE

In this paper, we propose an efficient nonlinear feature extraction method using kernel-based minimum squared error (KMSE). This improved method is referred to as reformative KMSE (RKMSE). In RKMSE, we use a linear combination of a small portion of samples that are selected from the training sample set, i.e. “significant nodes”, to approximate to the transform vector of KMSE in kernel space. As a result, RKMSE is much superior to naive KMSE in computational efficiency of feature extraction. Experimental results on several benchmark datasets illustrate that RKMSE can efficiently classify the data with high recognition correct rate.

Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions

Texture features are useful for segmentation of high-resolution satellite imagery. This paper presents an efficient feature extraction method that considers the spatial and cross-band relationships of pixels in multispectral or colour images. The texture feature of an image region is represented by the joint distribution of two texture measures calculated from the first two principal components (PCs). Similarly, the spectral feature of the region is the joint distribution of greyscale pixel values of the two PCs. The texture distributions computed by a rotation invariant form of local binary patterns (LBP) and spectral distributions are adaptively combined into coarse-to-fine segmentation based on integrated multiple features (SIMF). The feasibility and effectiveness of the SIMF segmentation approach is evaluated with multispectral high-resolution satellite imagery and colour textured mosaic images under different conditions.