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.     

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.     

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.     

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...     

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.     

Double Gaussian mixture model for image segmentation with spatial relationships

In this paper, we present a finite mixture model based on a Gaussian distribution for image segmentation. There are four advantages to the proposed model. First, compared with the standard Gaussian mixture model (GMM), the proposed model effectively incorporates spatially relationships between the pixels using a Markov random field (MRF). Second, the proposed model is similar to GMM, but has a simple representation and is easier to implement than some existing models based on MRF. Third, the contextual mixing proportion of the proposed model is explicitly modelled as a probabilistic vector and can be obtained directly during the inference process. Finally, the expectation maximization algorithm and gradient descent approach are used to maximize the log-likelihood function and infer the unknown parameters of the proposed model. The performance of the proposed model at image segmentation is compared with some state-of-the-art models on various synthetic noisy grayscale images and real-world color images.     

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。     

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。     

Fuzzy local Gaussian mixture model for brain MR image segmentation

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.     

改进高斯混合模型的运动目标检测算法

传统的高斯模型无法检测比较复杂的场景或速度较低的运动目标,因此提出基于改进高斯混合模型的运动目标检测算法.使用多个高斯模型表示运动目标图像内各像素点特征,并基于图像内各像素点与高斯混合模型相匹配则视其为背景点,反之为前景点原理,更新高斯混合模型.通过更新前景模型并计算短时稳定度指标,提高运动目标检测效果,通过确定高斯分...     

Multiscale fragile watermarking based on the Gaussian mixture model.

In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking.     

Three stochastic measurement schemes for direction-of-arrival estimation using compressed sensing method

We focus on the design of the measurement schemes in the compressed sensing (CS) method for direction-of-arrival estimation, and three stochastic measurement schemes are considered. In the perspectives of average apertures and incoherences, we give a detailed mathematical analysis for these schemes. The superiorities in incoherences for these schemes are illustrated, compared with the popularly used random Gaussian measurement scheme. Then we demonstrate that the newly used Poisson disk sampling scheme and uniform jittered sampling scheme can obtain relatively large average apertures by using a proposed computational method. Finally, several simulations are implemented to evaluate the performances of the CS methods with these stochastic measurement schemes.     

Adaptive quantization using piecewise companding and scaling for Gaussian mixture

Quantization is fundamental to analog-to-digital converter (ADC) and signal compression. In this paper, we propose an adaptive quantizer with piecewise companding and scaling for signals of Gaussian mixture model (GMM). Our adaptive quantizer operates under three modes, each of which corresponds to different types of GMM. Moreover, we propose a reconfigurable architecture to implement our adaptive quantizer in an ADC. We also use it to quantize images and design the tone mapping algorithm for high dynamic range (HDR) image compression. Our experimental results show that (1) the proposed quantizer is able to achieve performance close to the optimal quantizer (i.e., Lloyd–Max quantizer for GMM) in the sense of mean squared error (MSE), at much lower computational cost than it; (2) the proposed quantizer is able to achieve much better MSE performance than a uniform quantizer, at a cost similar to the uniform quantizer. The proposed adaptive quantizer holds great potential in the appilcations of the existing ADC and HDR image compression.     

高斯混合模型自适应微光图像增强

为了改善微光情况下可见光图像传感器输出图像的质量,提出了一种基于高斯混合模型的自适应微光图像增强算法。对图像的直方图进行混合高斯建模,应用改进的期望最大化算法对直方图拟合,从而获取高斯混合模型的最优参数,然后根据各个聚类的交点将直方图分区,最后确定输出图像所属聚类的映射关系,同时应用保持最大熵方法逼近人类视觉特性映射函数得到最终的增强图像。实验结果表明,此图像增强模型能自适应确定最佳聚类个数,提高直方图拟合的运算速度,一帧图像平均处理时间为0.37s,在相关信息熵和纹理信息等的客观评价中,增强结果明显优于传统方法,有效地提高了微光图像的对比度,同时保持了图像的细节。     

Cooperative wideband spectrum sensing based on sequential compressed sensing

Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on sequential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simultaneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required measurements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effectiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the performance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.     

基于改进高斯混合模型的运动目标检测与跟踪

为了提高运动目标检测与跟踪的精确性与可靠性,提出一种基于改进高斯混合模型的运动目标检测与跟踪方法。首先,建立改进高斯混合背景模型,对运动目标图像进行分块处理,利用相连帧的连续性对运动目标图像的参数更新,提取完整的运动目标并进行分割;其次,将给定的当前帧像素点与目标图像进行匹配,减少高斯混合模型的分布数量和计算量,根据分块处理后的运动目标的大小、形状以及颜色信息完成运动目标全局匹配,实现运动目标的实时检测与跟踪。实验结果表明,与目前的高斯混合模型对运动目标检测与跟踪的方法相比,所提方法计算过程较为简单,具有更快的检测速度和更可靠的检测结果。     

基于改进的混合高斯模型的运动目标跟踪研究

对传统混合高斯背景模型作了改进,消除了缓慢运动目标对背景模型的影响,其中提出了目标间差分方法区分出前后帧变化区,对不同区域采用不同的学习权重更新策略。通过实验证明,该改进算法提高了背景模型的健壮性,在跟踪系统中获得较好效果。     

基于混合高斯模型与核密度估计的目标检测

背景建模与目标检测是视频跟踪的重要步骤和基础,非参数核密度估计与混合高斯模型是背景建模与目标检测的经典方法。文中首先介绍了高斯模型与核密度估计的基本原理及各自的优缺点,然后提出了一种核密度-混合高斯模型级联算法,利用核密度估计快速分割前景与背景区域,再由混合高斯模型对于无法精确建模的区域进行二次判定,有效综合了二者各自的优点。仿真结果表明,该算法具有良好的实时性和鲁棒性。