Learning multiview face subspaces and facial pose estimation using independent component analysis.

An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.     

独立成分分析的多光谱图像融合算法

为解决图像空间信息的问题,文章提出了一种独立成分分析的多光谱图像融合算法,将多光谱图像的RGB 3个波段和近红外图像共4个波段进行独立成分分析变化,并对其做加权平均得到主图像信息,将主图像信息与全色图像加权求平均得到一副新的图像,然后将这幅图像还原到4个波段得到融合后的结果图像。     

独立成分分析的多光谱图像融合算法

为解决图像空间信息的问题,文章提出了一种独立成分分析的多光谱图像融合算法,将多光谱图像的RGB 3个波段和近红外图像共4个波段进行独立成分分析变化,并对其做加权平均得到主图像信息,将主图像信息与全色图像加权求平均得到一副新的图像,然后将这幅图像还原到4个波段得到融合后的结果图像。     

Imaging brain dynamics using independent component analysis

The analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain     

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.     

一种基于独立分量分析的图像融合方法

独立分量分析是一种基于高阶统计量的信号分析方法,近年来在图像处理和信号处理领域发挥着越来越重要的作用,因此也逐渐得到了人们广泛的关注和研究。本文提出了一种基于独立分量分析的图像融合方法。文中首先介绍了3种常用的图像融合方法：加权平均法、主成分分析法和小波变换法;然后在ICA模型的基础上提出了基于独立分量分析的图像融合方法;最后通过实验验证了该算法的有效性。     

Jammer suppression in DS-CDMA arrays using independent component analysis

We propose using Independent Component Analysis (ICA) as an advanced pre-processing tool for blind suppression of interfering jammer signals in direct sequence spread spectrum communication systems utilizing antenna arrays. The role of ICA is to provide a jammer-mitigated signal to the conventional detection. If the jammer signal is weak or absent, preprocessing by ICA is not advisable. Therefore we also consider two possible switching schemes, called pre-switching and post-switching, which activate the ICA-based jammer canceller only when it is expected to improve conventional detection. ICA-RAKE pre-switching is less complex, while post-switching performs better, especially when the jammer is pulsed in nature. Simulations are given to illustrate the achieved performance gains for single- and multi-path channels.     

基于独立成分分析的双麦克阵列语音增强算法

提出了利用频域的独立成分分析（Independent components analysis）算法分离语音信号和噪声信号,达到抑制噪声的效果.并且,针对ICA算法在噪声源集中的环境中效果较好,在噪声源分散的环境中性能有所退化的情况,基于时域带噪信号的ICA算法提出频域带噪信号的ICA算法.最后利用最小均方误差估计谱幅度算法（Minimum mean square error）去除残留噪声,达到较好的语音增强效果.通过大量的实验数据测试,文中提出的基于ICA和MMSE短时谱幅度估计的双麦克语音增强算法在不同信噪比（Signal to Noise Ratio）下,都取得了良好的降噪效果.     

Motion artifact reduction in photoplethysmography using independent component analysis

Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.     

Non-linear independent component analysis using series reversion and Weierstrass network

The problem of blind separation of independent sources in non-linear mixtures is considered and the focus of this work is on a new type of non-linear mixture in which a linear mixing matrix is sandwiched between two mutually reverse non-linearities. The demixing system culminates to a novel Weierstrass network that is shown to successfully restore the original source signals under the non-linear mixing conditions. The corresponding parameter learning algorithm for the proposed network is presented through formal mathematical derivation. The authors show for the first time a new result based on the theory of forward series and series reversion that is integrated into a neural network to implement the proposed demixer. Simulations, including both synthetic and recorded signals, have been carried out to verify the efficacy of the proposed method. It is shown that the Weierstrass network outperforms other tested independent component analysis (ICA) methods (linear ICA, radial-basis function and multilayer perceptron network) in terms of speed and accuracy.     

EEG-based drowsiness estimation for safety driving using independent component analysis

Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.     

Multiple classifiers for color flag and trademark image retrieval

A novel region-based multiple classifier color image retrieval system is presented. In our approach, a region-growing technique is first employed to cluster connected color pixels with the same color in an image to form color regions which are the primitive elements utilized in our proposed approach. Then, three complementary region-based classifiers that we developed are selected in the classifier selection stage, which include color classifier, shape classifier, and relational classifier. In each classifier, a virtue probability representing the probability that an image is similar to the query image is defined. Thereafter a set of virtue probabilities is calculated in each classifier. Next, the measurement dependent methods are applied to combine the virtue probabilities of classifiers in the decision combination stage. The dynamic selection scheme designed in the decision combination stage can further improve the system performance dramatically. Experimental results reveal the feasibility and validity of our proposed approach in solving the color image retrieval problem     

Content-based image retrieval using H.264 intra coding features

Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images.     

基于RMFA-ICA的人脸识别

针对有噪的ICA模型,提出一种有限制的平均场近似(restrictive m ean field approxim ation,RMFA)的算法来求解ICA模型参数和源信号的估计问题.在传统MFA-ICA算法的基础上,提出将ICA中的模型参数和源信号均限制为非负,目的是使得提取出的特征更独立,更利于识别.通过手写体数字和仿真模拟人脸图形以及ORL人脸数据进行实验,将RMFA-ICA算法与传统的ICA算法和无限制的MFA-ICA算法进行比较,对于手写体数字和仿真模拟人脸图形,RMFA-ICA算法能分离出更独立的特征,对于ORL人脸数据,其结果表明,利用RMFA-ICA算法明显优于传统ICA算法和无限制MFA-ICA算法识别结果.     

Dictionary-based color image retrieval using multiset theory

Dictionaries have recently attracted a great deal of interest as a new powerful representation scheme that can describe the visual content of an image. Most existing approaches nevertheless, neglect dictionary statistics. In this work, we explore the linguistic and statistical properties of dictionaries in an image retrieval task, representing the dictionary as a multiset. This is extracted by means of the LZW data compressor which encodes the visual patterns of an image. For this reason the image is first quantized and then transformed into a 1D string of characters. Based on the multiset notion we also introduce the Normalized Multiset Distance (NMD), as a new dictionary-based dissimilarity measure which enables the user to retrieve images with similar content to a given query. Experimental results demonstrate a significant improvement in retrieval performance compared to related dictionary-based techniques or to several other image indexing methods that utilize classical low-level image features.     

Generalized manifold-ranking-based image retrieval.

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR [12], our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques.     

改进的参考独立分量分析算法

针对参考独立分量分析收敛速度较慢的问题,提出了两种基于改进的快速收敛牛顿迭代方法的参考独立分量分析方法。该方法首先对观测信号进行白化预处理,避免观测信号矩阵求逆运算,减少了算法的计算量;然后采用修正的具有三阶收敛速度的牛顿迭代方法对参考独立分量分析的代价函数进行优化,推导出快速收敛的参考独立分量分析算法。仿真实验结果表明,改进后的算法是有效的,算法收敛速度相对原算法提高了1.7倍,相对现有算法提高了1倍,而且误差更小。     

基于IGA的AR序列叠加过程的分解与复原

本文研究由若干个AR信号序列叠加形成的多道时间序列的分解与复原问题.首先从信号的独立性出发,利用信号的高阶统计信息,采用独立成分分析(Independent Component Analysis,ICA)中的广义信息最大化(Infomax)算法寻找一可逆矩阵将混合信号进行分离,然后再根据AR序列的偏相关系数的截尾性,利用Levinson递推公式估计出AR序列的参数,最后通过模拟实验验证了此方法的有效性.     

Improving auditory steady-state response detection using independent component analysis on multichannel EEG data

Over the last decade, the detection of auditory steady-state responses (ASSR) has been developed for reliable hearing threshold estimation at audiometric frequencies. Unfortunately, the duration of ASSR measurement can be long, which is unpractical for wide scale clinical application. In this paper, we propose independent component analysis (ICA) as a tool to improve the ASSR detection in recorded single-channel as well as multichannel electroencephalogram (EEG) data. We conclude that ICA is able to reduce measurement duration significantly. For a multichannel implementation, near-optimal performance is obtained with five-channel recordings.