Results 1  10
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33
A bayesian hierarchical model for learning natural scene categories
 In CVPR
, 2005
"... We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region ..."
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Cited by 948 (15 self)
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We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a “theme”. In previous work, such themes were learnt from handannotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes. 1.
Shape topics: A compact representation and new algorithm for 3D partial shape retrieval
 In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06
"... This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bagofwords model. The main contributions of this ..."
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Cited by 50 (2 self)
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This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bagofwords model. The main contributions of this paper are threefold as follows: 1) a partial shape dissimilarity measure is proposed to rank shapes according to their distances to the input query, without using any timeconsuming alignment procedure; 2) by applying the probabilistic text analysis technique, a highly compact representation "Shape Topics " and accompanying algorithms are developed for efficient 3D partial shape retrieval, the mapping from "Shape Topics " to "object categories " is established using multiclass SVMs; and 3) a method for evaluating the performance of partial shape retrieval is proposed and tested. To our best knowledge, very few existing methods are able to perform well online partial shape retrieval for large 3D shape repositories. Our experimental results are expected to validate the efficacy and effectiveness of our novel approach.
Unsupervised learning
 Advanced Lectures on Machine Learning
, 2004
"... We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modelling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mix ..."
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Cited by 30 (0 self)
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We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modelling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, statespace models, and many variants and extensions. We derive the EM algorithm and give an overview of fundamental concepts in graphical models, and inference algorithms on graphs. This is followed by a quick tour of approximate Bayesian inference, including Markov chain Monte Carlo (MCMC), Laplace approximation, BIC, variational approximations, and expectation propagation (EP). The aim of this chapter is to provide a highlevel view of the field. Along the way, many stateoftheart ideas and future directions are also reviewed. Contents 1
Hidden Markov Models with Stick Breaking Priors
"... The number of states in a hidden Markov model is an important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend to a variational Bayesian solution. We present a full ..."
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Cited by 12 (2 self)
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The number of states in a hidden Markov model is an important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend to a variational Bayesian solution. We present a fully conjugate, Bayesian approach to determining the number of states in a hidden Markov model, which does have a variational solution. The infinitestate hidden Markov model presented here utilizes a stickbreaking construction for each row of the state transition matrix, which allows for a sparse utilization of the same subset of observation parameters by all states. In addition to our variational solution, we discuss retrospective and collapsed Gibbs sampling methods for MCMC inference. We demonstrate our model on a music recommendation problem containing 2,250 pieces of music from the classical, jazz and rock genres.
A generic approach to topic models
 In Proc. European Conf. on Mach. Learn. / Principles and Pract. of Know. Discov. in Databases (ECML/PKDD
, 2009
"... Abstract. This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as “mixture networks”, a domainspecific compact alternative to Bayesian networks. Besides ..."
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Cited by 9 (3 self)
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Abstract. This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as “mixture networks”, a domainspecific compact alternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of this representation is its straightforward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm. 1
On Variational Message Passing on Factor Graphs
, 2007
"... In this paper, it is shown how (naive and structured) variational algorithms may be derived from a factor graph by mechanically applying generic message computation rules; in this way, one can bypass errorprone variational calculus. In prior work by Bishop et al., Xing et al., and Geiger, directed ..."
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Cited by 9 (1 self)
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In this paper, it is shown how (naive and structured) variational algorithms may be derived from a factor graph by mechanically applying generic message computation rules; in this way, one can bypass errorprone variational calculus. In prior work by Bishop et al., Xing et al., and Geiger, directed and undirected graphical models have been used for this purpose. The factor graph notation amounts to simpler generic variational message computation rules; by means of factor graphs, variational methods can straightforwardly be compared to and combined with various other messagepassing inference algorithms, e.g., Kalman filters and smoothers, iterated conditional modes, expectation maximization (EM), gradient methods, and particle filters. Some of those combinations have been explored in the literature, others seem to be new. Generic message computation rules for such combinations are formulated. 1.
Approximate Algorithms for Credal Networks with Binary Variables
, 2007
"... This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and setvalued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among ..."
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Cited by 8 (1 self)
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This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and setvalued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the IPE and SV2U algorithms are respectively based on Localized Partial Evaluation and variational techniques.
Variational Maximum A Posteriori by Annealed Mean Field Analysis
"... Abstract—This paper proposes a novel probabilistic variational method with deterministic annealing for the maximum a posteriori (MAP) estimation of complex stochastic systems. Since the MAP estimation involves global optimization, in general, it is very difficult to achieve. Therefore, most probabil ..."
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Cited by 7 (2 self)
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Abstract—This paper proposes a novel probabilistic variational method with deterministic annealing for the maximum a posteriori (MAP) estimation of complex stochastic systems. Since the MAP estimation involves global optimization, in general, it is very difficult to achieve. Therefore, most probabilistic inference algorithms are only able to achieve either the exact or the approximate posterior distributions. Our method constrains the mean field variational distribution to be multivariate Gaussian. Then, a deterministic annealing scheme is nicely incorporated into the mean field fixpoint iterations to obtain the optimal MAP estimate. This is based on the observation that when the covariance of the variational Gaussian distribution approaches to zero, the infimum point of the KullbackLeibler (KL) divergence between the variational Gaussian and the real posterior will be the same as the supreme point of the real posterior. Although global optimality may not be guaranteed, our extensive synthetic and real experiments demonstrate the effectiveness and efficiency of the proposed method. Index Terms—Mean field variational analysis, deterministic annealing, maximum a posteriori estimation, graphical model, Markov network. 1
Efficient visual search of images and videos
, 2007
"... This thesis investigates visual search of videos and image collections, where the query is specified by an image or images of the object. We study efficient retrieval of particular objects, human faces, and object classes. Particular objects are represented by a set of viewpoint invariant region des ..."
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Cited by 6 (1 self)
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This thesis investigates visual search of videos and image collections, where the query is specified by an image or images of the object. We study efficient retrieval of particular objects, human faces, and object classes. Particular objects are represented by a set of viewpoint invariant region descriptors, so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. Efficient retrieval is achieved by employing methods from statistical text retrieval, including inverted file systems, and text and document frequency weightings. This requires a visual analogy of a word – ‘a visual word’ – and it is provided by vector quantizing the region descriptors. We also develop a representation for 3D and deforming objects, suitable for retrieval, based on multiple exemplars naturally spanning (i) different visual aspects of a 3D object and thereby implicitly representing its 3D structure, or (ii) different appearances of a deforming object. Multiple exemplar models are built automatically from real world videos, using novel tracking and motion segmentation techniques. For retrieval of faces of a particular person in video, we focus on closetofrontal faces delivered
1 Expectation Maximization as Message Passing—Part I: Principles and Gaussian Messages
, 2009
"... Abstract—It is shown how expectation maximization (EM) may be viewed as a message passing algorithm in factor graphs. In particular, a new general EM message computation rule is identified. As a factor graph tool, EM may be used to break cycles in a factor graph, and “nice ” messages may in some cas ..."
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Abstract—It is shown how expectation maximization (EM) may be viewed as a message passing algorithm in factor graphs. In particular, a new general EM message computation rule is identified. As a factor graph tool, EM may be used to break cycles in a factor graph, and “nice ” messages may in some cases be obtained where the standard sumproduct messages are unwieldy. As an exemplary application, the paper considers linear Gaussian state space models with multipliers. Such multipliers arise naturally from unknown model coefficients. A main attraction of EM in such cases is that it results in purely Gaussian message passing algorithms. These Gaussian EM messages are tabulated for several (scalar, vector, matrix) multipliers that frequently appear in applications. I.