An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Abstract In this paper, a novel Bayesian network representation is proposed for signature verification. It is different from the traditional Bayesian network, in that its nodes are divided into two classes: common hypotheses and alternative hypotheses, to ensure the constructed network is a tree structure. The network not only captures the conditional probability associated with each node, but also the topological relations among components associated with the network nodes, so that the uncertainty in structure description and the dependencies among components are encoded. Results based on eight persons’ signatures indicate that the method offers a considerable improvement in performance over some other popular techniques for signature verification. Since the uncertainty and interaction of pattern components are common phenomena in pattern representation and matching, the approach may also be enlightening for other pattern classification problems. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> The realization of natural human-computer interfaces suffers from a wide range of restrictions concerning noisy data, vague meanings, and context dependence. An essential aspect of everyday communication is the ability of humans to ground verbal interpretations in visual perception. Thus, the system has to be able to solve the correspondence problem of relating verbal and visual descriptions of the same object. This contribution proposes a new and innovative solution to this problem using Bayesian networks. In order to capture vague meanings of adjectives used by the speaker, psycholinguistic experiments are evaluated. Object recognition errors are taken into account by conditional probabilities estimated on test sets. The Bayesian network is dynamically built up from verbal object description and is evaluated by an inference technique combining bucket elimination and conditioning. Results show that speech and image data is interpreted more robustly in the combined case than in the case of isolated interpretations. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Prior research in scene classi cation has focused on mapping a set of classic low-level vision features to semantically meaningful categories using a classi er engine. In this paper, we propose improving the established paradigm by using a simpli ed low-level feature set to predict multiple semantic scene attributes that are integrated probabilistically to obtain a nal indoor/outdoor scene classi cation. An initial indoor/outdoor prediction is obtained by classifying computationally e"cient, low-dimensional color and wavelet texture features using support vector machines. Similar low-level features can also be used to explicitly predict the presence of semantic features including grass and sky. The semantic scene attributes are then integrated using a Bayesian network designed for improved indoor/outdoor scene classi cation. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection. <s> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> In this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach. <s> BIB006 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> This paper presents Analysis and assessment of welding images and different techniques involved in detection of flaws in weldments, The efficient and reliable detection of defects is one of the important Task in Nondestructive testing (NDT) And Nondestructive evaluation (NDE) but before that concept of analysis of welded images and its classification is very important, Multiple methods are available for extraction of features. It has been widely known and also these methods serve as Powerful tools for Weldments. Image segmentation, Artifical neural network, pattern recognition, morphological processing, Using Bayesian networks And Advanced Neuro Fuzzy Techniques can be Analyzed and implemented as per the desired requirements in NDT and NDE <s> BIB007 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> In this paper, we address the problem of scene modeling for performing video surveillance. The problem consists of using the trajectories, obtained by observing objects in a scene, to construct a scene model that can be used to distinguish a normal and an acceptable behavior from a atypical one. In this regard, the proposed method is divided into a training phase and a testing phase. During the training phase, the input trajectories are used to identify different paths or routes commonly taken by the objects in a scene. Important discriminative features are then extracted from these identified paths to learn a dynamic Bayesian network (DBN). During the testing phase, the learned network is used to classify the incoming trajectories based on their size, location, speed, acceleration, and spatio-temproal curvature characteristics. The proposed method (i) handles trajectories of varying lengths, (ii) automatically detects the number of paths presents in a scene, and (iii) introduces the novel usage of the DBN, which is very intuitive and accurately captures the dynamics of the scene. We show results on four datasets of varying lengths and successfully show results for both path clustering and anomalous behavior detection. <s> BIB008 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. Image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention. <s> BIB009 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Traditional content-based image classifications often fail to meet a user's need due to the ‘semantic gap’ between the texture features and the semantic features of the image. Content-based indexing and retrieval of images requires a proper semantic description for image content. This paper presents a novel approach based on semantic features and Bayesian networks for image classification. A mapping between low-level visual information and higher-level semantic space by using priori knowledge is created for image classification using Bayesian networks. We performed experiments on a set of images which are collected from web pages and simulation results show feasibility and effectiveness. <s> BIB010 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> The recent increased interest in information fusion methods for solving complex problem, such as in image analysis, is motivated by the wish to better exploit the multitude of information, available from different sources, to enhance decision-making. In this paper, we propose a novel method, that advances the state of the art of fusing image information from different views, based on a special class of probabilistic graphical models, called causal independence models. The strength of this method is its ability to systematically and naturally capture uncertain domain knowledge, while performing information fusion in a computationally efficient way. We examine the value of the method for mammographic analysis and demonstrate its advantages in terms of explicit knowledge representation and accuracy (increase of at least 6.3% and 5.2% of true positive detection rates at 5% and 10% false positive rates) in comparison with previous single-view and multi-view systems, and benchmark fusion methods such as naive Bayes and logistic regression. <s> BIB011 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Representation of Bayesian Network in Image Processing <s> Identifying interactions among brain regions from structural magnetic-resonance images presents one of the major challenges in computational neuroanatomy. We propose a Bayesian data-mining approach to the detection of longitudinal morphological changes in the human brain. Our method uses a dynamic Bayesian network to represent evolving inter-regional dependencies. The major advantage of dynamic Bayesian network modeling is that it can represent complicated interactions among temporal processes. We validated our approach by analyzing a simulated atrophy study, and found that this approach requires only a small number of samples to detect the ground-truth temporal model. We further applied dynamic Bayesian network modeling to a longitudinal study of normal aging and mild cognitive impairment — the Baltimore Longitudinal Study of Aging. We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group. <s> BIB012
BN is used in various image processing applications such as in image segmentation BIB007 , BIB009 , development of unified framework BIB011 , BIB004 , classification image based information BIB010 , BIB003 , BIB008 , interacted regions analysis of an image BIB012 with enhanced recognition rate BIB006 , traffic incident detection BIB005 and signature verification BIB001 . BN is supportive in establishing the relationships among associated objects BIB002 . Let I be an image with p pixels states. In image segmentation, an original natural image can be referred to as an observed and segmented image with a limited number of regions. Original image is denoted as B p and an observed image is denoted as A p . Random fields of space can be represented as B p ={b 1 , b 2 , b 3 , ..., b n } and A p ={a 1 , a 2 , a 3 ,..., a n }. To represent BN in this context, the probability of an original image, P(B=b), can be referred to as priori probability. According to Bayes formula, P(B=b/A=a) can be referred to posterior probability. This observable fact can be expressed as follows: where,  and  are hyper-parameters and z={z 1 , z 2 , z 3 , ..., z n }.