An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences in Image Processing <s> This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences in Image Processing <s> Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reflecting the goodness-of-fit of the structure to the data. The search procedure tries to identify network structures with high scores. Heckerman et al. (1995) introduce a Bayesian metric, called the BDe metric, that computes the relative posterior probability of a network structure given data. In this paper, we show that the search problem of identifying a Bayesian network—among those where each node has at most K parents—that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB006 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB007 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB008 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB009 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> BIB010 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> B. Drawing Inferences 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> B. Drawing Inferences 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
In general, a direct application of BN is not promising as inferences in BN is a NP-complete problem BIB002 . NP-completeness means an exponential increase in complexity with number of variables n. However, exponential computational complexity can be minimized with the help of quality measures like K2 scoring function BIB001 . A lower complexity of inference results in a simpler algorithm. According to Cox  and Luttrell et al.  , the proposition of inference directs to use probabilities to get consistency in inferences. Inference direction approach in  ,  has three vital stages.  Select a state space,  Assign a joint probability density functions,  Perform inferences by computing conditional probability density functions, The above stages are referred to as Bayesian method. Bayesian method splits state space into two or more sub-spaces to discriminate between data and model parameters. Joint probability density function is used to make predictions over past data, future data and model parameters after computing the conditional probability density function of future given historical data. In this context, image segmentation BIB010 is performed with the use of image regions, edges, vertices and angles to extract new knowledge. The main objective of BIB010 is to predict the best possible spot with user intervention. In addition, relationships are identified in BIB003 by integrating speech and images to associate spatial and information classes. Besides, a unified image understanding framework with the use of BN in BIB006 addresses the problem of uncertainty in vision problem. The goal is to build inference engine for domain knowledge and training of data of few case studies. In BIB011 , the characteristics of regions in complimentary images are determined probabilistically for boosting decision making. Low-level image of indoor and outdoor scenes are classified by predicting semantic features. In addition, video surveillance for scene modeling and novel approach based on semantics is performed to classify trajectories and images with the use of BN in BIB009 and BIB005 . Also, a dynamic BN and MRI analysis is carried out in BIB012 to find interacted regions. Moreover, a descriptor combination method based shape measures and shape descriptors are proposed in BIB008 to enhance recognition rate. BN-based video de-interlacing method integrated relationships practically into causal framework with limited number of parameters. Traffic incident detection BIB007 and signature verification BIB004 are inferred for traffic management with strong evidence-based reasoning and quick reflection in signature verification with the help of BN.