Independent Component Analysis Basis Research Articles

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies.

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed “hyperscanning.” Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

Action recognition based on overcomplete independent components analysis

Existing works on action recognition rely on two separate stages: (1) designing hand-designed features or learning features from video data; (2) classifying features using a classifier such as SVM or AdaBoost. Motivated by two observations: (1) independent component analysis (ICA) is capable of encoding intrinsic features underlying video data; and (2) videos of different actions can be easily distinguished by their intrinsic features, we propose a simple but effective action recognition framework based on the recently proposed overcomplete ICA model. After a set of overcomplete ICA basis functions are learned from the densely sampled 3D patches from training videos for each action, a test video is classified as the class whose basis functions can reconstruct the sampled 3D patches from the test video with the smallest reconstruction error. The experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state-of-the-art works.

Saliency detection using sparse and nonlinear feature representation.

An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.

Defect Detection in Solar Modules Using ICA Basis Images

Solar power has become an attractive alternative of electricity energy. Solar cells that form the basis of a solar power system are mainly based on multicrystalline silicon. A set of solar cells are assembled and interconnected into a large solar module to offer a large amount of electricity power for commercial applications. Many defects in a solar module cannot be visually observed with the conventional CCD imaging system. This paper aims at defect inspection of solar modules in electroluminescence (EL) images. The solar module charged with electrical current will emit infrared light whose intensity will be darker for intrinsic crystal grain boundaries and extrinsic defects including micro-cracks, breaks and finger interruptions. The EL image can distinctly highlight the invisible defects but also create a random inhomogeneous background, which makes the inspection task extremely difficult. The proposed method is based on independent component analysis (ICA), and involves a learning and a detection stage. The large solar module image is first divided into small solar cell subimages. In the training stage, a set of defect-free solar cell subimages are used to find a set of independent basis images using ICA. In the inspection stage, each solar cell subimage under inspection is reconstructed as a linear combination of the learned basis images. The coefficients of the linear combination are used as the feature vector for classification. Also, the reconstruction error between the test image and its reconstructed image from the ICA basis images is also evaluated for detecting the presence of defects. Experimental results have shown that the image reconstruction with basis images distinctly outperforms the ICA feature extraction approach. It can achieve a mean recognition rate of 93.4% for a set of 80 test samples.

Phase Space Feature Based on Independent Component Analysis for Machine Health Diagnosis

This paper proposes a new feature extraction method based on Independent Component Analysis (ICA) and reconstructed phase space. The ICA-based phase space feature unifies the system dynamics embedded in vibration signal and higher-order statistics expressed in phase spectrum and hence, is effective for machine health diagnosis. The new feature extraction is done in three steps: first, the Phase Space Reconstruction (PSR) is performed to reconstruct a phase space with the dimension covering dynamic structure information; second, the ICA bases are trained by a number of constructed phase points; and finally, the new feature is quantitatively calculated by evaluating the correlation property of transformed coefficients based on ICA bases. The presented feature contains plentiful phase information with the training pattern, which is often under evaluated when using existing methods. It has excellent pattern representation property and can be applied for signal classification and assessment. Experiments in an automobile transmission gearbox validate the effectiveness of the new method.

HOW TO IDENTIFY AND SEPARATE BRIGHT GALAXY CLUSTERS FROM THE LOW-FREQUENCY RADIO SKY

In this work we simulate the $50-200$ MHz radio sky that is constrained in the field of view ($5^{\circ}$ radius) of the 21 Centimeter Array (21CMA), by carrying out Monte-Carlo simulations to model redshifted cosmological reionization signals and strong contaminating foregrounds, including emissions from our Galaxy, galaxy clusters, and extragalactic point sources. As an improvement of previous works, we consider in detail not only random variations of morphological and spectroscopic parameters within the ranges allowed by multi-band observations, but also evolution of radio halos in galaxy clusters, assuming that relativistic electrons are re-accelerated in the ICM in merger events and lose energy via both synchrotron emission and inverse Compton scattering with CMB photons. By introducing a new approach designed on the basis of independent component analysis (ICA) and wavelet detection algorithm, we prove that, with a cumulative observation of one month with the 21CMA array, about $80\%$ of galaxy clusters with central brightness temperatures of $> 10~{\rm K}$ at 65 MHz can be safely identified and separated from the overwhelmingly bright foreground. We find that the morphological and spectroscopic distortions are extremely small as compared to the input simulated clusters, and the reduced $\chi^2$ of brightness temperature profiles and spectra are controlled to be $\lesssim 0.5$ and $\lesssim 1.3$, respectively. These results robustly indicate that in the near future a sample of dozens of bright galaxy clusters will be disentangled from the foreground in 21CMA observations, the study of which will greatly improve our knowledge about cluster merger rates, electron acceleration mechanisms in cluster radio halos, and magnetic field in the ICM.

A robust method based on ICA and mixture sparsity for edge detection in medical images

In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, the Lp norm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.

A robust calibration modeling strategy for analysis of interference‐subject spectral data

AbstractPreprocessing and correction of mixture spectra have been an important issue with regard to the removal of undesired systematic variation due to variations in environmental, instrumental, or sample conditions. In this article, a new robust calibration modeling strategy is proposed on the basis of independent component analysis (ICA). It aims at separating the interference‐subject parasitic subspace from the interference‐immune common subspace among all considered cases. The common subspace is further divided into two orthogonal parts according to their relationship with quality: one is quality‐irrelevant and the other is quality‐informative, in which, only the second part is employed for quality prediction. Focusing on each subspace, it identifies distinct types of underlying source components underlying different spectra subspaces, analyzes their characteristics and roles, and accordingly models them for different applications, respectively. This approach provides a comprehensive insight into the inherent nature of interference‐subject mixture spectra. Furthermore, several model statistics are defined to give quantitative indication on the effectiveness of the correction strategy. The feasibility and performance of the proposed method are illustrated with data from laboratory experiments. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Training image-analysis bases improves ‘image fusion’

Modern technology has enabled development of low-cost wireless imaging sensors of various modalities that can be deployed for monitoring scenes. This advance has been strongly motivated by both military and civilian applications, including those related to health care, battlefield surveillance, and environmental monitoring. Multimodal sensors provide diverse degradation, thermal, and visual characteristics. Image fusion combines visual information from various sources into a single representation to facilitate processing by an operator or computervision system. Fusion techniques can be divided into spatial and transform-domain methods.1 The latter enable efficient identification of an image’s salient features. Transformations that have been suggested for image fusion include dual-tree wavelet transforms and pyramid decomposition.1 We recently proposed2, 3 an image-fusion framework—featuring improved performance—that is based on image-analysis bases trained using independent-component analysis (ICA). Receptive fields of simple cells in the mammalian primary visual cortex are usually spatially localized, oriented, and bandpass. Such filter responses can be derived from unsupervised learning of independent visual features or sparse linear codes for natural scenes.4 Basis training employing ICA4 for image denoising through sparse-code shrinkage improved performance with respect to the use of wavelets. The bases were trained by extracting a population of local patches from similar-content images and then processed by the FastICA algorithm4 to estimate the transformation and its inverse. ICA bases are closely related to wavelets and Gabor functions because they represent localized-edge features. They havemore degrees of freedom than wavelets, however, because they adapt to arbitrary orientations. Discrete and dual-tree wavelet transforms have only two and six distinct orientations, respectively.4 ICA bases do not offer a multilevel representation—as do wavelets or pyramid decomposition—nor are they shift invariant. This invariance can Figure 1. Proposed image-fusion framework. T{ } and T−1{ }: Independent-component analysis (ICA)-trained transformations and their inverse. uk(t): Image coefficients in the ICA domain.

Overcomplete topographic independent component analysis

Topographic and overcomplete representations of natural images/videos are important problems in computational neuroscience. We propose a new method using both topographic and overcomplete representations of natural images, showing emergence of properties similar to those of complex cells in primary visual cortex (V1). This method can be considered as an extension of model in Hyvärinen et al. [Topographic independent component analysis, Neural Comput. 13 (7) (2001) 1527–1558], which uses complete topographic representation. We utilize a sparse and approximately uncorrelated decompositions and define a topographic structure on coefficients (the dot products between basis vectors and whitened observed data vectors). The overcomplete topographic basis vectors can be learned via estimation of independent component analysis (ICA) model based on the prior assumption upon basis vectors. Computer simulations are provided to show the relationship between our model and the basic properties of complex cells in V1 cortex. The learned bases are shown to have better coding efficiency than ordinary topographic ICA (TICA) bases.

Multispectral infrared image classification using filters derived from independent component analysis

Spectral-spatial independent component analysis (ICA) basis functions of visible color images are similar to some processing elements in the human visual systems in that they resemble Gabor filters and show color opponencies. In this research we studied combined spectral-spatial ICA basis functions of multispectral mid wave infrared (MWIR) images. These ICA spectral-spatial basis functions were then used as filters to extract features from multispectral MWIR images for classification. The images were captured in the 3.0–5.0 µm, 3.7–4.2 µm, and 4.0–4.5 µm bands using a multispectral MWIR camera. In the proposed algorithm, phase relationships between the basis functions indicate how the extracted features from the different spectral band images can be combined. We used classification performance to compare features obtained by filtering using multispectral ICA basis functions, multispectral principal component analysis basis functions, and Gabor filters.

Detection of signal transients using independent component analysis and its application in gearbox condition monitoring

This paper addresses feature extraction of the higher-order statistics, which can effectively characterize the transients, using independent component analysis (ICA) for the one-dimensional measured vibration signal, and then proposes a novel automatic technique for detecting the transients in vibration signals with the low signal-to-noise ratio by ICA feature extraction. The basic principle of the ICA-based transient detection method is that the independent components (ICs) coefficients of the transients and the noise can be effectively distinguished by their different sparseness properties. Specifically, the proposed method mainly includes three steps: training the ICA basis features from the signal segments, denoising the sparse ICs coefficients using the shrinkage function deduced by the maximum a posteriori (MAP) estimation, and reconstructing the transient segments by the shrunken coefficients through the ICA basis functions. Experimental results through the simulated signal analysis and the vibration signal analysis show that the ICA-based method is very effective for transient detection outperforming the traditional methods and is valuable for gearbox condition monitoring and fault diagnosis.

Articulated Hand Motion Tracking Using ICA-based Motion Analysis and Particle Filtering

This paper introduces a new representation of hand motions for tracking and recognizing hand-finger gestures in an image sequence. A human hand has many joints, for example our hand model has 15, and its high dimensionality makes it difficult to model hand motions. To make things easier, it is important to represent a hand motion in a low dimensional space. Principle component analysis (PCA) has been proposed to reduce the dimensionality. However, the PCA basis vectors only represent global features, which are not optimal for representing intrinsic features. This paper proposes an efficient representation of hand motions by independent component analysis (ICA). The ICA basis vectors represent local features, each of which corresponds to the motion of a particular finger. This representation is more efficient in modeling hand motions for tracking and recognizing handfinger gestures in an image sequence. We will demonstrate the effectiveness of the method by tracking a hand in real image sequences.

On the use of independent component analysis for image compression

This paper addresses the use of independent component analysis (ICA) for image compression. Our goal is to study the adequacy (for lossy transform compression) of bases learned from data using ICA. Since these bases are, in general, non-orthogonal, two methods are considered to obtain image representations: matching pursuit type algorithms and orthogonalization of the ICA bases followed by standard orthogonal projection. Several coder architectures are evaluated and compared, using both the usual SNR and a perceptual quality measure called picture quality scale. We consider four classes of images (natural, faces, fingerprints, and synthetic) to study the generalization and adaptation abilities of the data-dependent ICA bases. In this study, we have observed that: bases learned from natural images generalize well to other classes of images; bases learned from the other specific classes show good specialization. For example, for fingerprint images, our coders perform close to the special-purpose WSQ coder developed by the FBI. For some classes, the visual quality of the images obtained with our coders is similar to that obtained with JPEG2000, which is currently the state-of-the-art coder and much more sophisticated than a simple transform coder. We conclude that ICA provides a excellent tool for learning a coder for a specific image class, which can even be done using a single image from that class. This is an alternative to hand tailoring a coder for a given class (as was done, for example, in the WSQ for fingerprint images). Another conclusion is that a coder learned from natural images acts like an universal coder, that is, generalizes very well for a wide range of image classes.

A New Iris Recognition Method Using Independent Component Analysis

In a conventional method based on quadrature 2D Gabor wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper we propose a new feature extraction algorithm based on independent component analysis (ICA) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns. Additionally, we introduce a method to refine the ICA basis functions for improving the recognition performance. Experimental results show that our proposed method has a similar equal error rate as a conventional method based on the Gabor wavelets, and the iris code size of our proposed methods is five times smaller than that of the Gabor wavelets.

Pixel-based and region-based image fusion schemes using ICA bases

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixel-based or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity.

A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision

We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.

Database retrieval for similar images using ICA and PCA bases

Similar-image retrieval systems are newly presented and examined. The systems use ICA bases (independent component analysis bases) or PCA bases (principal component analysis bases). These bases can contain source image's information, however, the indeterminacy of ordering and amplitude on the bases exists due to the PCA and ICA problem formulation per se. But, this paper successfully avoids this difficulty by using weighted inner products of similar bases. A set of opinion test is carried out on 18 systems according to the combination of {similarity measures (ICA, PCA, color histogram), color spaces (RGB, YIQ, HSV), filtering (with, without)}. The color histogram method is a traditional method. The opinion test shows that the presented method of {ICA, HSV, without filtering} is the best. Runners-up are {ICA, HSV or RGB or YIQ, with filtering}. The traditional method is judged to be much inferior. Thus, this paper's method is found quite effective to the similar-image retrieval from large databases.

Independent component analysis in a local facial residue space for face recognition

In this paper, we propose an Independent Component Analysis (ICA) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most Principal Component Analysis (PCA) based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the “residual face space”. We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.

10.1162/jmlr.2003.4.7-8.1365

This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a priori sets of time-domain basis functions learned by independent component analysis (ICA) to the separation of mixed source signals observed in a single channel. The inherent time structure of sound sources is reflected in the ICA basis functions, which encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources.