An integrated approach for scene understanding based on Markov Random Field model

In this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.     

Higher-order SVD analysis for crowd density estimation

This paper proposes a new method to estimate the crowd density based on the combination of higher-order singular value decomposition (HOSVD) and support vector machine (SVM). We first construct a higher-order tensor with all the images in the training set, and apply HOSVD to obtain a small set of orthonormal basis tensors that can span the principal subspace for all the training images. The coordinate, which best describes an image under this set of orthonormal basis tensors, is computed as the density character vector. Furthermore, a multi-class SVM classifier is designed to classify the extracted density character vectors into different density levels. Compared with traditional methods, we can make significant improvements to crowd density estimation. The experimental results show that the accuracy of our method achieves 96.33%, in which the misclassified images are all concentrated in their neighboring categories.     

A probabilistic framework for copy-move forgery detection based on Markov Random Field

Multimedia Tools and Applications - Copy-move forgery is one of the most common kind of image tampering where some part of an image is copied, may be with minor modifications, pasted to another...     

An information-theoretic wavelet-based texture descriptor using Gaussian Markov random field models

Multimedia Tools and Applications - Texture characterization and identification is a key issue for a variety of computer vision and image processing applications. Current techniques developed for...     

Markov随机场与Gaussian曲线在MR图像分割中的应用

针对扫描的人脑组织MR图像边缘分辨率低、模糊性大的特点,本文提出了一种基于模糊Markov随机场和Gaussian曲线相结合的MR图像最佳阈值分割方法。该方法通过对图像的像素邻域属性的统计将模糊论引入其中,建立模糊Markov随机场,并利用Gaussian曲线对二维直方图最佳一维投影进行拟合,确定出图像中各脑组织的二维阈值点,在二维直方图上实现对脑组织的分割。通过实验表明,本算法能够有效提高脑组织的分辨率,对噪声的鲁棒性、结果区域的连通性相对于一维Otsu和二维Otsu算法都有了很大的提高。     

Probability density estimation using a Gaussian clustering algorithm

A version of the Tråvén's [1] Gaussian clustering algorithm for normal mixture densities is studied. Unlike in the case of the Tråvén's algorithm, no constraints on covariance structure of mixture components are imposed. Simulations suggest that the modified algorithm is a very promising method of estimating arbitrary continuous d-dimensional densities. In particular, the simulations have shown that the algorithm is robust against assuming the initial number of mixture components to be too large.This work was supported in part by the State Committee for Scientific Research (KBN) under grant PB 0589/P3/94/06. It was completed while the second author was on leave to the Department of Statistics, Rice University, Houston, Texas.     

基于纹理和高斯密度特征的图像检索算法

直接从DCT域中提取图像的特征是提高图像的检索效率的方法.直接从压缩域中提取图像的高斯密度,即计算图像在8个方向上的分段累加值,形成一个8*4的二维向量,再结合图像的纹理特征来进行图像检索.为了验证算法的可行性,建立了10000幅图像的图像库.实验结果表明,该方法能够准确地检索出目标图像,有效地提高了图像检索的精度和速度.     

马尔科夫随机场化的光照一致图像合成方法

针对图像合成中源图像与目标图像光照环境不一致造成直接合成图像不逼真的问题,提出一种基于马尔科夫随机场的光照一致图像合成方法.首先基于加权的泊松克隆方法构建梯度保持的光滑约束,削弱传统的泊松克隆方法在合成边界源图像和目标图像光照差异变化剧烈时产生的渗透效应;然后基于直方图对齐的方法构建光照一致的数据约束,保持合成图像前、背景亮度主轴的一致性;最后根据合成边界源图像的边缘特性以及源图像和目标图像光照差异变化的剧烈程度自适应地调整2项约束的权重,并采用融合局部和全局一致性的学习算法对构建的马尔科夫随机场函数进行快速求解.实验结果表明,该方法产生的合成效果在梯度特征保持方面以及亮度一致性方面均优于传统的泊松克隆方法,同时收敛速度得到了提高.     

基于小波与高斯Markov随机场组合的轮廓纹理分割

为综合多尺度纹理模型和高斯型Markov随机场纹理模型各自的优点，本文提出了组合这两种模型的方法，Mallat的经验法、高斯型Markov随机场纹理模型和组合方法的对比实验表明，当纹理结构包含微结构时，组合方法分割纹理轮廓的性能最好、     

Multi-scale and multi-column convolutional neural network for crowd density estimation

Multimedia Tools and Applications - In order to accurately identify objects of different sizes, we propose an efficient Multi-Scale and Multi-Column Convolutional Neural Network (MSMC) to estimate...     

A hybrid deep network based approach for crowd anomaly detection

Multimedia Tools and Applications - In this paper, we present a hybrid deep network based approach for crowd anomaly detection in videos. For improved performance, the proposed approach exploits...     

Semi-supervised classification based on Markov Random Field and Robust Error Function

We focus on semi-supervised classifier, where a decision rule is to be learned from labeled and unlabeled data. A model to semi-supervised classification is proposed to overcome the problem induced by mislabeled samples. A new energy function based on robust error function is used in Markov Random Field. Also two algorithms based on iterative condition mode and markov chain monte carlo respectively are designed to infer the label of both labeled and unlabeled samples. Our experiments demonstrate that the proposed method is efficient for real-world dataset.     

Gaussian kernels for density estimation with compositional data

Common simplifications of the bandwidth matrix cannot be applied to existing kernels for density estimation with compositional data. In this paper, kernel density estimation methods are modified on the basis of recent developments in compositional data analysis and bandwidth matrix selection theory. The isometric log-ratio normal kernel is used to define a new estimator in which the smoothing parameter is chosen from the most general class of bandwidth matrices on the basis of a recently proposed plug-in algorithm. Both simulated and real examples are presented in which the behaviour of our approach is illustrated, which shows the advantage of the new estimator over existing proposed methods.     

Moving object detection using spatio-temporal multilayer compound Markov Random Field and histogram thresholding based change detection

Multimedia Tools and Applications - In this article, we propose a Multi Layer Compound Markov Random Field (MLCMRF) Model to spatially segment different image frames of a given video sequence. The...     

Depth and edge auxiliary learning for still image crowd density estimation

Pattern Analysis and Applications - Crowd counting plays a significant role in crowd monitoring and management, which suffers from various challenges, especially in crowd-scale variations and...     

Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields

There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the Expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares regressions for the individual pixels in the data set. Use of the EM algorithm also gives a substantial performance gain over Markov Chain Monte Carlo-based estimation approaches.     

A contextual-based segmentation of compact PolSAR images using Markov Random Field (MRF) model

As the first major step in each object-oriented feature extraction approach, segmentation plays an essential role as a preliminary step towards further and higher levels of image processing. The primary objective of this paper is to illustrate the potential of Polarimetric Synthetic Aperture Radar (PolSAR) features extracted from Compact Polarimetry (CP) SAR data for image segmentation using Markov Random Field (MRF). The proposed method takes advantage of both spectral and spatial information to segment the CP SAR data. In the first step of the proposed method, k-means clustering was applied to over-segment the image using the appropriate features optimally selected using Genetic Algorithm (GA). As a similarity criterion in each cluster, a probabilistic distance was used for an agglomerative hierarchical merging of small clusters into an appropriate number of larger clusters. In the agglomerative clustering approach, the estimation of the appropriate number of clusters using the data log-likelihood algorithm differs depending on the distance criterion used in the algorithm. In particular, the Wishart Chernoff distance which is independent of samples (pixels) tends to provide a higher appropriate number of clusters compared to the Wishart test statistic distance. This is because the Wishart Chernoff distance preserves detailed data information corresponding to small clusters. The probabilistic distance used in this study is Wishart Chernoff distance which evaluates the similarity of clusters by measuring the distance between their complex Wishart probability density functions. The output of this step, as the initial segmentation of the image, is applied to a Markov Random Field model to improve the final segmentation using vicinity information. The method combines Wishart clustering and enhanced initial clusters in order to access the posterior MRF energy function. The contextual image classifier adopts the Iterated Conditional Mode (ICM) approach to converge to a local minimum and represent a good trade-off between segmentation accuracy and computation burden. The results showed that the PolSAR features extracted from CP mode can provide an acceptable overall accuracy in segmentation when compared to the full polarimetry (FP) and Dual Polarimetry (DP) data. Moreover, the results indicated that the proposed algorithm is superior to the existing image segmentation techniques in terms of segmentation accuracy.     

Distance and density based clustering algorithm using Gaussian kernel

Clustering is an important field for making data meaningful at various applications such as processing satellite images, extracting information from financial data or even processing data in social sciences. This paper presents a new clustering approach called Gaussian Density Distance (GDD) clustering algorithm based on distance and density properties of sample space. The novel part of the method is to find best possible clusters without any prior information and parameters. Another novel part of the algorithm is that it forms clusters very close to human clustering perception when executed on two dimensional data. GDD has some similarities with today’s most popular clustering algorithms; however, it uses both Gaussian kernel and distances to form clusters according to data density and shape. Since GDD does not require any special parameters prior to run, resulting clusters do not change at different runs. During the study, an experimental framework is designed for analysis of the proposed clustering algorithm and its evaluation, based on clustering performance for some characteristic data sets. The algorithm is extensively tested using several synthetic data sets and some of the selected results are presented in the paper. Comparative study outcomes produced by other well-known clustering algorithms are also discussed in the paper.     

Gaussian activation functions using Markov chains

We extend, in two major ways, earlier work in which sigmoidal neural nonlinearities were implemented using stochastic counters. 1) We define the signal to noise limitations of unipolar and bipolar stochastic arithmetic and signal processing. 2) We generalize the use of stochastic counters to include neural transfer functions employed in Gaussian mixture models. The hardware advantages of (nonlinear) stochastic signal processing (SSP) may be offset by increased processing time; we quantify these issues. The ability to realize accurate Gaussian activation functions for neurons in pulsed digital networks using simple hardware with stochastic signals is also analyzed quantitatively.