A Feature Selection and Extraction Method for Uyghur Handwriting-Based Writer identification

In this research we concentrate on adaptive texture feature extractors which are automatically extracted from an image collection. We use independent component analysis (ICA) to extract independent component filters (ICF). ICF have previously been shown to include edge filters, having properties similar to the receptive fields of simple cells of the human visual cortex. In this thesis we evaluate the utility of ICF-based collection-specific features in the context of content-based image retrieval (CBIR), with the view to demonstrate the viability of using such automatic collection-specific features. We find that global features extracted using a small number of ICF outperform those extracted by a bank of Gabor filters, even when very large number of Gabors are used. We also present comparisons against a variety of state-of-the-art features and show that ICF-based features perform better than these, without the need for any hand-tuning. ICA extracts large number of filters. In order to find a useful smaller subset we evaluate a previously-published variance-based filter selection method. We identify the shortcomings of this method. Our proposed improvements to filter-based feature selection and extraction, response scaling and locally normalised convolution, was a result of trying to address some of these shortcomings. Even with these improvements the variance-based method has an intrinsic flaw of being susceptible to selecting redundant filters. We propose a new filter selection method based on normalised cross-correlation and clustering. We show that our method selects a more useful subset compared to the previously published variance-based method. We also propose a salient-point based local feature which uses ICF and show that our proposed feature performs better than SIFT features and also the global ICF features. We further illustrate the utility of ICFSIFT by developing a grid-based adaptive local feature and we demonstrate that ICFSIFT extracts different and useful information compared to the ICF-based global and grid-based local features. After applying the techniques proposed in this thesis to four standard texture collections, we show that, while hand-tuned features can work well for their target collection, the automatic adaptive features perform better for the general case.

Color histograms are effective for representing color visual features. However, the high dimensionality of feature vectors results in high computational cost. Several transformations, including principal component analysis (PCA), have been proposed to reduce the dimensionality. PCA reduce the dimensionality by projecting the data to a subspace which contains most of the variance. It is restricted to an orthogonal transformation and may not be the optimal to represent the intrinsic features of data. In this paper, we apply independent component analysis (ICA) to extract the features in color histograms. PCA is applied to reduce the dimensionality and then ICA is performed on the low-dimensional PCA subspace. Furthermore, spatial information is incoporated by performing ICA on a color coherent vector (CCV). The experimental results show that the proposed method outperform other methods based on SVD of quadratic matrix or PCA, in terms of retrieval accuracy.

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

Extracting efiective features to describe texture is always a key problem in writer identiflcation. This paper proposes a novel method for texture feature extraction by integrating Gabor fllter and Gauss Markov Random Field (GMRF). That is to say, the handwriting images are flrstly flltered by a bank of Gabor fllters in which global features such as directional information can be detected; then GMRF models are developed for every flltered image that is ∞exible enough to capture the local spatial structure and the model parameters of all GMRFs are concatenated as texture features for writer identiflcation, and flnally, Support Vector Machine (SVM) is applied to evaluate the performance of calligraphic artist identiflcation. The experimental results show that this method can achieve a classiflcation rate of 98%, which has outperformed the traditional Gabor fllter method.

Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recognition, facial image compression. There are many methods have been proposed in the literature for the facial feature extraction task. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. Therefore, a combination of different feature extraction methods which can integrate the complementary information should lead to improve the efficiency of feature extraction stage. In this paper we describe a methodology for improving the efficiency of feature extraction stage based on the association of two methods: geometric feature based method and Independent Component Analysis (ICA) method. Comparison of two methods of facial feature extraction: geometric feature based method combined with PCA method (called GPCA) versus geometric feature based method combined with ICA method (called GICA) on CalTech dataset has demonstrated the efficiency of GICA method. Our results show that GICA achieved good performance 96.57% compared to 94.70% of GPCA method. Furthermore, we compare two methods mentioned above on our dataset, with performance of GICA being 98.94% better 96.78% of GPCA method. The experiment results have confirmed the benefits of the association geometric feature based method and ICA method in facial feature extraction.KeywordsFace recognitionindependent component analysis (ICA)principal component analysis (PCA)geometric features

Extracting effective features for texture classification is always a difficult problem in texture analysis. We present a method for texture features extraction by independent component analysis (ICA) of Gabor features (called ICAG). It has three distinguished aspects. First, Gabor wavelet transformation first produces distinct textural features characterized by spatial locality, scale, and orientation selectivity. Second, principal component analysis (PCA) and ICA then reduce the dimensionality and redundancy of these features. Thus, the resulting independent components are taken as texture features for classification. Third, in the ICA procedure, two different frameworks are discussed. Framework I, called ICAG I, regards pixels as random variables and represents them as a column vector by reshaping all the transformed images row by row, while framework II, called ICAG II, treats the statistical features, namely, the mean and standard deviation, as random variables. Thus, the statistical features of all the transformed images construct a column vector. Finally, comparative experiments between ICAG and the other traditional methods—ICA and Gabor wavelets—are performed on two composite images (five narrowband textures and five natural textures with broader bands). The results indicate that ICAG provides the best performance and ICAG II is more efficient and applicable.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Multivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

이 논문은 고차원의 데이터를 저 차원으로 줄이는 방법 중 하나인 특징추출에 대한 방법들의 특성을 비교한다. 비교대상 방법은 전통적인 PCA(Principal Component Analysis)방법과 시각피질의 특성을 보인다고 알려진 ICA(Independent Component Analysis), 국소기반인식을 구현한 NMF(Non-negative Matrix Factorization), 그리고 이의 성능을 개선한 sNMF(Sparse NMF)로 정하였다. 추출된 특징들의 특성을 시각적으로 확인하기 위하여 필기체 숫자 영상을 대상으로 특징추출을 수행하였으며, 인식기에 적용한 효과의 확인을 위하여 추출된 특징을 다층퍼셉트론에 학습시켜보았다. 각 방법의 특성을 비교한 결과는 응용하고자 하는 문제에서 어떤 특징을 추출하기 원하느냐에 따라 특징추출 방법을 선정할 때 유용할 것이다. In this paper, feature extraction methods, which is one field of reducing dimensions of high-dimensional data, are empirically investigated. We selected the traditional PCA(Principal Component Analysis), ICA(Independent Component Analysis), NMF(Non-negative Matrix Factorization), and sNMF(Sparse NMF) for comparisons. ICA has a similar feature with the simple cell of V1. NMF implemented a "parts-based representation in the brain" and sNMF is a improved version of NMF. In order to visually investigate the extracted features, handwritten digits are handled. Also, the extracted features are used to train multi-layer perceptrons for recognition test. The characteristic of each feature extraction method will be useful when applying feature extraction methods to many real-world problems.

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,n)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

Principal component analysis (PCA) is linear method for feature extraction that is known as Karhonen Loove method. PCA was first proposed to recognize face by Turk and Pentland, and was also known as eigenface in 1991 [Turk, 1991]. However, PCA has some weaknesses. The first, it cannot capture the simplest invariance of the face image [Arif et al., 2008b] , when this information is not provided in the training data. The last, the result of feature extraction is global structure [Arif, 2008]. The PCA is very simple, has overcome curse of dimensionality problem, this method have been known and expanded by some researchers to recognize face such as Linear Discriminant Analysis (LDA)[Yambor, 2000; A.M. Martinez, 2003; J.H.P.N. Belhumeur 1998], Linear Preserving Projection that known Lapalacianfaces [Cai, 2005; Cai et al, 2006; Kokiopoulou, 2004; X. He et al., 2005], Independent Component Analysis, Kernel Principal Component Analysis [Scholkopf et al., 1998; Sch olkopf 1999], Kernel Linear Discriminant Analysis (KLDA) [Mika, 1999] and maximum feature value selection of nonlinear function based on Kernel PCA [Arif et al., 2008b]. As we know, PCA is dimensionality reduction method based on object appearance by projecting an original ndimensional (row*column) image into k eigenface where k<<n. Although PCA have been developed into some methods, but in some cases, PCA can outperform LDA, LPP and ICA when it uses small sample size.

&lt;p&gt;Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed. &lt;/p&gt;

Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model