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836
Automatic linguistic indexing of pictures by a statistical modeling approach
 PAMI
"... Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in computer vision and contentbased image retrieval. In this paper, we introduce a statistical modeling approach to this problem. Categorized images are used to train a dictionary of hundreds of ..."
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Cited by 300 (25 self)
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on the characterizing stochastic process is computed. A high likelihood indicates a strong association. In our experimental implementation, we focus on a particular group of stochastic processes, that is, the twodimensional multiresolution hidden Markov models (2D MHMMs). We implemented and tested our ALIP (Automatic
A Probabilistic Framework for SemiSupervised Clustering
, 2004
"... Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supe ..."
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Cited by 275 (14 self)
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such supervision. Such methods use the constraints to either modify the objective function, or to learn the distance measure. We propose a probabilistic model for semisupervised clustering based on Hidden Markov Random Fields (HMRFs) that provides a principled framework for incorporating supervision into prototype
Maximumlikelihood estimation for hidden Markov models
 STOCHASTIC PROCESSES AND THEIR APPLICATIONS
, 1992
"... Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximumlikelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximumli ..."
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Cited by 115 (0 self)
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Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximumlikelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum
Hidden Markov models and disease mapping
 Journal of the American Statistical Association
, 2001
"... We present new methodology to extend Hidden Markov models to the spatial domain, and use this class of models to analyse spatial heterogeneity of count data on a rare phenomenon. This situation occurs commonly in many domains of application, particularly in disease mapping. We assume that the counts ..."
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Cited by 93 (7 self)
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We present new methodology to extend Hidden Markov models to the spatial domain, and use this class of models to analyse spatial heterogeneity of count data on a rare phenomenon. This situation occurs commonly in many domains of application, particularly in disease mapping. We assume
Hidden Markov Random Fields
, 1993
"... A noninvertible function of a first order Markov process, or of a nearest neighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. A ..."
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Cited by 5 (1 self)
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A noninvertible function of a first order Markov process, or of a nearest neighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility
unknown title
"... A major problem of segmentation of magnetic resonance imaging is that intensities are not standardized like in computed tomography. In this article we will present a new method for MRI intensity standardization by aligning histograms of higher dimensions. So the correction process is independent ..."
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from spatial coherences and prior segmentations of the reference and newly acquired images. Although the approach is not limited to a specic application or area of the body, it is utilized for fast classication of brain tissue. Therefore, reference statistics are computed once using a hidden Markov
HiddenArticulator Markov Models For Speech Recognition
 IN PROC. IEEE INTL. CONF. ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
, 2000
"... In traditional speech recognition using Hidden Markov Models (HMMs), each state represents an acoustic portion of a phoneme. We explore the concept of an articulator based HMM, where each state represents a particular articulatory configuration [Erler 1996]. In this paper, we present a novel articul ..."
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Cited by 96 (19 self)
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In traditional speech recognition using Hidden Markov Models (HMMs), each state represents an acoustic portion of a phoneme. We explore the concept of an articulator based HMM, where each state represents a particular articulatory configuration [Erler 1996]. In this paper, we present a novel
Approximating Hidden Gaussian Markov Random Fields
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 2003
"... This paper discusses how to construct approximations to a unimodal hidden Gaussian Markov random field on a graph of dimension n when the likelihood consists of mutually independent data. We demonstrate that a class of nonGaussian approximations can be constructed for a wide range of likelihood ..."
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Cited by 24 (4 self)
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This paper discusses how to construct approximations to a unimodal hidden Gaussian Markov random field on a graph of dimension n when the likelihood consists of mutually independent data. We demonstrate that a class of nonGaussian approximations can be constructed for a wide range of likelihood
Hidden Markov Models
"... Introduction HMMs are statistical modeling techniques with a strong mathematical foundation and nds application in elds such as Biology for modeling of RNA sequences [Grate] and Speech Recognition for creating speech unit models such as words [Rabiner89]. HMMs are an extension to the wellknown con ..."
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Introduction HMMs are statistical modeling techniques with a strong mathematical foundation and nds application in elds such as Biology for modeling of RNA sequences [Grate] and Speech Recognition for creating speech unit models such as words [Rabiner89]. HMMs are an extension to the well
Equations for hidden Markov models
, 2009
"... We will outline novel approaches to derive model invariants for hidden Markov and related models. These approaches are based on a theoretical framework that arises from viewing random processes as elements of the vector space of string functions. Theorems available from that framework then give rise ..."
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Cited by 2 (0 self)
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We will outline novel approaches to derive model invariants for hidden Markov and related models. These approaches are based on a theoretical framework that arises from viewing random processes as elements of the vector space of string functions. Theorems available from that framework then give
Results 11  20
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836