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46
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 494 (18 self)
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. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles. 1 Introduction Since its beginning in early 1960's, computer vision research has been evolving from heuristic design of algorithms to syste...
Probabilistic independence networks for hidden Markov probability models
, 1996
"... Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been develop ..."
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Cited by 186 (12 self)
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Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a selfcontained review of the basic principles of PINs. It is shown that the wellknown forwardbackward (FB) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.
A Markov Random Field modelbased approach to unsupervised texture segmentation using local and global spatial statistics
, 1993
"... The general problem of unsupervised textured image segmentation remains a fundamental but not entirely solved issue in image analysis. Many studies have proven that statistical modelbased texture segmentation algorithms yield good results provided that the model parameters and the number of region ..."
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Cited by 41 (4 self)
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The general problem of unsupervised textured image segmentation remains a fundamental but not entirely solved issue in image analysis. Many studies have proven that statistical modelbased texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this paper, we present an unsupervised texture segmentation method which does not require a priori knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmentedstate Markov Random Field, including an outlier class which enables dynamic creation of new regions during the optimization process. A bayesian estimate of this map is computed using a deterministic relaxation algorithm. The whole segmentation procedure is controlled by one single p...
Segmentation of Moving Objects in Video Sequences
, 2001
"... various contentbased functionalities for multimedia applications. To support such functionalities, as well as to improve coding efficiency, MPEG4 relies on a decomposition of each frame of an image sequence into video object planes (VOPs). Each VOP corresponds to a single moving object in the scen ..."
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Cited by 38 (0 self)
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various contentbased functionalities for multimedia applications. To support such functionalities, as well as to improve coding efficiency, MPEG4 relies on a decomposition of each frame of an image sequence into video object planes (VOPs). Each VOP corresponds to a single moving object in the scene. This paper presents a new method for automatic segmentation of moving objects in image sequences for VOP extraction. We formulate the problem as graph labeling over a region adjacency graph (RAG), based on motion information. The label field is modeled as a Markov random field (MRF). An initial spatial partition of each frame is obtained by a fast, floatingpoint based implementation of the watershed algorithm. The motion of each region is estimated by hierarchical region matching. To avoid inaccuracies in occlusion areas, a novel motion validation scheme is presented. A dynamic memory, based on object tracking, is incorporated into the segmentation process to maintain temporal coherence of the segmentation. Finally, a labeling is obtained by maximization of the a posteriori probability of the MRF using motion information, spatial information and the memory. The optimization is carried out by highest confidence first (HCF). Experimental results for several video sequences demonstrate the effectiveness of the proposed approach. Index Termsâ€”Markov random fields, MPEG4, video segmentation, VOP extraction, watershed segmentation. I.
Restriction of a Markov Random Field on a Graph and Multiresolution Statistical Image Modeling
 IEEE Trans. Inform. Theory
, 1996
"... The association of statistical models and multiresolution data analysis in a consistent and tractable mathematical framework remains an intricate theoretical and practical issue. Several consistent approaches have been proposed recently to combine Markov Random Field (MRF) models and multiresolution ..."
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Cited by 26 (1 self)
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The association of statistical models and multiresolution data analysis in a consistent and tractable mathematical framework remains an intricate theoretical and practical issue. Several consistent approaches have been proposed recently to combine Markov Random Field (MRF) models and multiresolution algorithms in image analysis: renormalization group, subsampling of stochastic processes, MRF's defined on trees or pyramids, etc. For the simulation or a practical use of these models in statistical estimation, an important issue is the preservation of the local Markovian property of the representation at the different resolution levels. It is shown in this paper that this key problem may be studied by considering the restriction of a Markov random field (defined on some simple finite nondirected graph) to a part of its original site set. Several general properties of the restricted field are derived. The general form of the distribution of the restriction is given. "Locality" of the field...
Image interpretation Using Bayesian Networks
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... The problem of image interpretation is one of inference with the help of domain knowledge. In this correspondence, we formulate the problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function. We show that a Bayesian network can be used to represent thi ..."
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Cited by 22 (1 self)
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The problem of image interpretation is one of inference with the help of domain knowledge. In this correspondence, we formulate the problem as the maximum a posteriori (MAP) estimate of a properly defined probability distribution function. We show that a Bayesian network can be used to represent this p.d.f. as well as the domain knowledge needed for interpretation. The Bayesian network may be relaxed to obtain the set of optimum interpretations.
Incorporating multiple SVMs for automatic image annotation
 PATTERN RECOGNITION
, 2007
"... In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)based and globalfeaturebased SVMs, for annotation. The MILbased bag features are obtained by applying MIL on the image blo ..."
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Cited by 22 (2 self)
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In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)based and globalfeaturebased SVMs, for annotation. The MILbased bag features are obtained by applying MIL on the image blocks, where the enhanced diversity density (DD) algorithm and a faster searching algorithm are applied to improve the efficiency and accuracy. They are further input to a set of SVMs for finding the optimum hyperplanes to annotate training images. Similarly, global color and texture features, including color histogram and modified edge histogram, are fed into another set of SVMs for categorizing training images. Consequently, two sets of image features are constructed for each test image and are, respectively, sent to the two sets of SVMs, whose outputs are incorporated by an automatic weight estimation method to obtain the final annotation results. Our proposed annotation approach demonstrates a promising performance for an image database of 12 000 generalpurpose images from COREL, as compared with some current peer systems in the literature.
2 A Hierarchical and Contextual Model for Aerial Image Understanding
"... In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene and object levels to handle the difficult task of representing scene elements at different scales and add contextual constr ..."
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Cited by 21 (5 self)
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In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene and object levels to handle the difficult task of representing scene elements at different scales and add contextual constraints to resolve ambiguities in the scene interpretation. This allows the model to rule out inconsistent detections, like cars on trees, and to verify low probability detections based on their local context, such as small cars in parking lots. We also present a twostep algorithm for parsing aerial images that first detects objectlevel elements like trees and parking lots using color histograms and bagofwords models, and objects like roofs and roads using compositional boosting, a powerful method for finding image structures. We then activate the topdown scene model to prune false positives from the first stage. We learn this scene model in a minimax entropy framework and show unique samples from our prior model, which capture the layout of scene objects. We present experiments showing that hierarchical and contextual information greatly reduces the number of false positives in our results. 1. Introduction and Related
Spatial random tree grammars for modeling hierarchal structure in images with . . .
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2004
"... We present a novel probabilistic model for the hierarchical structure of an image and its regions. We call this model spatial random tree grammars (SRTGs). We develop algorithms for the exact computation of likelihood and maximum a posteriori (MAP) estimates and the exact expectationmaximization ( ..."
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Cited by 20 (3 self)
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We present a novel probabilistic model for the hierarchical structure of an image and its regions. We call this model spatial random tree grammars (SRTGs). We develop algorithms for the exact computation of likelihood and maximum a posteriori (MAP) estimates and the exact expectationmaximization (EM) updates for modelparameter estimation. We collectively call these algorithms the centersurround algorithm. We use the centersurround algorithm to automatically estimate the maximum likelihood (ML) parameters of SRTGs and classify images based on their likelihood and based on the MAP estimate of the associated hierarchical structure. We apply our method to the task of classifying natural images and demonstrate that the addition of hierarchical structure significantly improves upon the performance of a baseline model that lacks such structure.
A Markov Random Field Model for Object Matching under Contextual Constraints
 In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
, 1994
"... This paper presents a Markov random field (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework, the optimal solution is defined as the maximum a posteriori (MAP) estima ..."
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Cited by 20 (9 self)
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This paper presents a Markov random field (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework, the optimal solution is defined as the maximum a posteriori (MAP) estimate of the MRF. The posterior distribution is derived based on sound mathematical principles from theories of MRF and probability, which is in contrast to heuristic formulations. An experimental result is presented. 1 Introduction In object recognition, an object is usually represented by a set of primitives or features. These features are attributed by their properties and are constrained to one another by contextual interrelations. Two issues must be addressed for successful recognition: how to use contextual constraints effectively and how to deal with uncertainties. Markov random field (MRF) theory provides a way of encoding contextual constraints. Since 1980's, there has been considera...