Semi-supervised internet network traffic classification using a Gaussian mixture model

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different internet applications. The major contributions are: (1) we propose a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications; (2) we achieve an optimum configuration for the GMM-based semi-supervised classification system. The effectiveness of these proposed approaches is demonstrated through experimental results.     

Wavelet-based texture retrieval using generalized Gaussian densityand Kullback-Leibler distance

We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity.     

Image change detection using Gaussian mixture model and genetic algorithm

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images of the same scene using Gaussian mixture model (GMM) and genetic algorithm (GA). The difference image data computed from multi-temporal satellite images of the same scene is modelled by using N components GMM. GA is used to estimate the parameters of the GMM. Then, the GMM of the difference image data is partitioned into two sets of distributions representing data distributions of “changed” and “unchanged” pixels by minimizing a cost function using GA. Bayesian inference is exploited together with the estimated data distributions of “changed” and “unchanged” pixels to achieve the final change detection result. The proposed method does not need any parameter tuning process, and is completely automatic. As a case study for the unsupervised change detection, multi-temporal advanced synthetic aperture radar (ASAR) images acquired by ESA Envisat on the recent flooding area in Bangladesh and parts of India brought on by two weeks of persistent rain and multi-temporal optical images acquired by Landsat 5 TM on a part of Alaska are considered. Change detection results are shown on real data and comparisons with the state-of-the-art techniques are provided.     

局部高斯尺度混合模型的傅里叶-小波图像降噪

提出了一种基于局部高斯尺度混合统计模型的傅里叶-小波图像降噪方法.所提出的降噪方法综合了两者的优点,考虑到噪声小波系数间的相关性,小波系数统计特性通过局部高斯尺度混合统计模型来刻画.实验结果表明,此法可有效去除噪声,并且能够克服传统的小波去噪效果与选用的小波基函数相关的局限性,和其他方法相比,无论从视觉上还是峰值信噪比上比较,此方法降噪效果明显较好.     

基于空间约束高斯混合模型的EPLL自然图像复原

为了提高基于块先验的自然图像复原效果,有效去除图像中的噪声和模糊,提出了一种基于空间约束高斯混合模型的块似然对数期望(Expected Patch Log Likelihood, EPLL)复原框架。基于图像块的空间分布信息,将图像块的空间约束高斯混合统计特性作为先验,在图像块复原的基础上实现整幅图像的全局优化复原。对比相关的图像复原方法,提出的方法去噪和去模糊效果更好,并且保图像细节。利用客观性能指标对复原结果进行评价。实验结果表明,提出的方法有效易行,而且复原图像表现出良好的可视效果。     

Performance analysis of satellite link using Gaussian mixture model under rain

The evolution of communication systems to the next generation, for example, B5G and 6G, demands an ultrareliable performance regardless of weather conditions. Such ultrareliable system design will require that the effects of adverse weather events on the communication system have to be computed more accurately so that physical layer compensation should be optimally and dynamically adaptive to such events. The performance of satellite links is severely affected by dynamic rain attenuation, and thus, accurate and reliable modeling of performance parameters is essential for dynamic fade countermeasures, especially above 10 GHz. In this work, we model the energy per bit to noise spectral density ratio ( $E_b/N_0$) using Gaussian mixture (GM) model during rainy events. The developed mathematical expression is used to accurately model the average $E_b/N_0$, bit error rate (BER), outage probability, and ergodic channel capacity of the link. The average BER, upper bound on BER, and average ergodic capacity of an M-ary phase shift keying scheme (MPSK) using the GM model of $E_b/N_0$ are derived to evaluate the performance of the link under such weather impairments. We then show the numerical results and analysis using the GM model of the measured $E_b/N_0$ data obtained with the AMoS-7 satellite at a site located in Israel.     

Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number

In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.     

一种采用混合高斯模型与贝叶斯判别的彩色图像人脸检测方法

介绍了一种采用混合高斯模型与贝叶斯判别的彩色人脸检测方法。该方法首先利用图像的彩色信息以及人脸的基本特征信息来进行人脸粗检测,得到图像中的多个候选人脸区域;然后对训练图像集中的全部候选人脸区域进行判别特征分析和混合高斯建模;最后采用贝叶斯判别方法对候选人脸区域进行判决,得到人脸检测结果。实验结果显示该方法具有较好的推广效率和工程应用前景。     

基于高斯混合模型的火焰检测算法

火灾作为对社会和环境危害最大的灾难,一直是人们重点防范的对象.但目前现有的火灾预警系统都存在误报率过高的问题.因此,文中提出了一种基于火焰闪烁动力学的火焰检测框架.在该框架中,火焰颜色分布模型采用高斯混合模型.此外,采用概率显著性分析方法和一维小波变换提取运动显著性和滤波后的时间序列作为特征,描述火焰的动态特性和闪烁特...     

复小波包域复数高斯尺度混合模型图像降噪

提出一种基于四树复小波包变换(QCWPT)复数高斯尺度混合(CGSM)模型的图像去噪新方法。含噪图像经多尺度QCWPT被分解成一个低频复数逼近子图和若干高频复数方向细节子图。在大尺度下,可认为低频逼近子图为信号复系数予以保留;对各尺度复数方向子图按照对数熵代价函数确定最优复小波包基。把能很好刻画小波系数边缘分布形状和局部邻域强相关性的高斯尺度混合(GSM)模型扩展到复小波域,新的CGSM模型具有很好刻画图像幅值和相位信息的能力,并利用该模型对复系数进行贝叶斯最小均方(BLS)估计,从而实现去噪目的。实验结果表明,无论是峰值信噪比(PSNR)指标还是视觉效果,本文方法的去噪性能均好于双树复小波变换(DCWT)、QCWPT和小波域GSM模型去噪,并且在有效去噪的同时,具有很好的图像边缘和细节保护能力。     

结合压缩感知模型的稀疏阵列波束形成方法

本文从稀疏阵列入手,将稀疏阵列接收数据模型转化为更高自由度下的单快拍接收数据模型,并将压缩感知模型引入稀疏阵列信号处理问题中,从理论上证明了其可行性。在等效单快拍数据下,利用稀疏重构算法准确估计信源方位和功率,进而对传统MVDR波束形成器进行优化。仿真结果表明,采用压缩感知模型实现稀疏阵列的波束形成,能够将稀疏阵列和压缩感知算法两者的优势结合,在阵列阵元数较少的条件下达到更高的自由度,同时具备良好的波束形成器性能。     

On using non-linear canonical correlation analysis for voice conversion based on Gaussian mixture model

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).T...     

Visual privacy-preserving level evaluation for multilayer compressed sensing model using contrast and salient structural features

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.     

基于变分模型的块压缩感知重构算法

为了提高现有块压缩感知重构算法的性能,提出了基于全变分和混合变分模型的块压缩感知（简称BCS-TV和BCS-MV）算法。该方法以块为单位进行图像采样,以自然图像正则项的稀疏性为先验条件,通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM),在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比,该算法的PSNR约提高1.5 dB,SSIM约提高0.05,运行速度较稳定,特别适合具有固定传输时延的多媒体数据处理场合。     

Fast reconstruction method for compressed sensing model with semi-tensor product

To reduce the storage space of random measurement matrix and improve the reconstruction efficiency for compressed sensing (CS),a new sampling approach for CS with semi-tensor product (STP-CS) was proposed.The proposed approach generated a low dimensional random measurement matrix to sample the sparse signals.Then the solutions of the sparse vector were estimated group by group with a l_q-minimization (0$\frac{M}{t}\text{×}\frac{N}{t}$ and decrease tow orders of magnitude of time that for conventional CS,while maintaining the reconstruction quality.Numerical results also show that the reconstruction time can be effectively improved 260 for the image size of 1 024×1 024.     

Image deblurring using derivative compressed sensing for optical imaging application

The problem of reconstruction of digital images from their blurred and noisy measurements is unarguably one of the central problems in imaging sciences. Despite its ill-posed nature, this problem can often be solved in a unique and stable manner, provided appropriate assumptions on the nature of the images to be recovered. In this paper, however, a more challenging setting is considered, in which accurate knowledge of the blurring operator is lacking, thereby transforming the reconstruction problem at hand into a problem of blind deconvolution. As a specific application, the current presentation focuses on reconstruction of short-exposure optical images measured through atmospheric turbulence. The latter is known to give rise to random aberrations in the optical wavefront, which are in turn translated into random variations of the point spread function of the optical system in use. A standard way to track such variations involves using adaptive optics. Thus, for example, the Shack-Hartmann interferometer provides measurements of the optical wavefront through sensing its partial derivatives. In such a case, the accuracy of wavefront reconstruction is proportional to the number of lenslets used by the interferometer and, hence, to its complexity. Accordingly, in this paper, we show how to minimize the above complexity through reducing the number of the lenslets while compensating for undersampling artifacts by means of derivative compressed sensing. Additionally, we provide empirical proof that the above simplification and its associated solution scheme result in image reconstructions, whose quality is comparable to the reconstructions obtained using conventional (dense) measurements of the optical wavefront.     

基于OpenCV与混合高斯建模的运动目标检测

针对静态背景下的视频运动序列,在研究现有的检测算法——帧间差分法与背景差分法的基础上,进一步研究了运动目标检测中背景动态建模的方法——混合高斯建模法,在此基础上提出了基于混合高斯模型与三帧差分的运动目标检测改进算法。由于使用背景差分法检测运动目标时,运动物体和阴影都将被看作运动的目标,于是研究了基于归一化RGB色彩模型的阴影处理方法,对阴影区域进行检测与去除。然后使用计算机视觉类库OpenCV结合Visual C＋＋6．0对上述算法进行实现,取得了很好的检测效果。     

基于改进的混合高斯模型的运动目标检测方法

为了改善混合高斯模型在光照突变时容易产生大量误检的缺陷,采用了一种高斯模型与均值法相结合并为前景像素建立计数器的方法。在建立背景模型时,运用多帧图像求平均值的方法初始化混合高斯模型的背景;为每帧图像的前景像素数建立计数器,并以此消除被误判为前景的区域;对检测出的前景区运用数学形态学处理,得到图像真正的前景区域。结果表明,该算法不仅克服了初始背景中的干扰,而且消除了光照突变时的误检,提高了运动目标的检测率。     

改进的自适应高斯混合模型运动目标检测算法

传统的高斯混合模型学习率和分布数取值固定,不能精确地描述变换的背景,同时存在数据冗余等问题,针对这些不足,进行了以下三个方面的改进。在模型初始化阶段,针对不同的环境设定各异的初始分布数;根据环境变化快慢程度,动态调整学习率的取值;不断更新高斯分布,删除不满足要求的模型,创建新的分布。实验结果表明,改进的自适应高斯混合模型较传统高斯混合模型,显著提高了运动物体检测的准确性。