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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 9 • Date Sept. 2004

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Displaying Results 1 - 18 of 18
  • [Front cover]

    Page(s): c1
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  • [Inside front cover]

    Page(s): c2
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  • A Bayesian approach to joint feature selection and classifier design

    Page(s): 1105 - 1111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (486 KB) |  | HTML iconHTML  

    This paper adopts a Bayesian approach to simultaneously learn both an optimal nonlinear classifier and a subset of predictor variables (or features) that are most relevant to the classification task. The approach uses heavy-tailed priors to promote sparsity in the utilization of both basis functions and features; these priors act as regularizers for the likelihood function that rewards good classification on the training data. We derive an expectation- maximization (EM) algorithm to efficiently compute a maximum a posteriori (MAP) point estimate of the various parameters. The algorithm is an extension of recent state-of-the-art sparse Bayesian classifiers, which in turn can be seen as Bayesian counterparts of support vector machines. Experimental comparisons using kernel classifiers demonstrate both parsimonious feature selection and excellent classification accuracy on a range of synthetic and benchmark data sets. View full abstract»

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  • FloatBoost learning and statistical face detection

    Page(s): 1112 - 1123
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1473 KB) |  | HTML iconHTML  

    A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported. View full abstract»

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  • An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision

    Page(s): 1124 - 1137
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3457 KB) |  | HTML iconHTML  

    Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes. View full abstract»

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  • Range image segmentation by an effective jump-diffusion method

    Page(s): 1138 - 1153
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1743 KB) |  | HTML iconHTML  

    This paper presents an effective jump-diffusion method for segmenting a range image and its associated reflectance image in the Bayesian framework. The algorithm works on complex real-world scenes (indoor and outdoor), which consist of an unknown number of objects (or surfaces) of various sizes and types, such as planes, conics, smooth surfaces, and cluttered objects (like trees and bushes). Formulated in the Bayesian framework, the posterior probability is distributed over a solution space with a countable number of subspaces of varying dimensions. The algorithm simulates Markov chains with both reversible jumps and stochastic diffusions to traverse the solution space. The reversible jumps realize the moves between subspaces of different dimensions, such as switching surface models and changing the number of objects. The stochastic Langevin equation realizes diffusions within each subspace. To achieve effective computation, the algorithm precomputes some importance proposal probabilities over multiple scales through Hough transforms, edge detection, and data clustering. The latter are used by the Markov chains for fast mixing. The algorithm is tested on 100 1D simulated data sets for performance analysis on both accuracy and speed. Then, the algorithm is applied to three data sets of range images under the same parameter setting. The results are satisfactory in comparison with manual segmentations. View full abstract»

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  • Simultaneous feature selection and clustering using mixture models

    Page(s): 1154 - 1166
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1638 KB) |  | HTML iconHTML  

    Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters. View full abstract»

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  • Simultaneous two-view epipolar geometry estimation and motion segmentation by 4D tensor voting

    Page(s): 1167 - 1184
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2610 KB) |  | HTML iconHTML  

    We address the problem of simultaneous two-view epipolar geometry estimation and motion segmentation from nonstatic scenes. Given a set of noisy image pairs containing matches of n objects, we propose an unconventional, efficient, and robust method, 4D tensor voting, for estimating the unknown n epipolar geometries, and segmenting the static and motion matching pairs into n, independent motions. By considering the 4D isotropic and orthogonal joint image space, only two tensor voting passes are needed, and a very high noise to signal ratio (up to five) can be tolerated. Epipolar geometries corresponding to multiple, rigid motions are extracted in succession. Only two uncalibrated frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) other than the pin-hole camera model is made. Our novel approach consists of propagating a local geometric smoothness constraint in the 4D joint image space, followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions. We have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved. Results on challenging data sets are presented. View full abstract»

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  • Utilization of hierarchical, stochastic relationship modeling for Hangul character recognition

    Page(s): 1185 - 1196
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1206 KB) |  | HTML iconHTML  

    In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by which strokes as well as relationships are stochastically represented by utilizing the hierarchical characteristics of target characters. A character is defined by a multivariate random variable over the components and its probability distribution is learned from a training data set. To overcome difficulties of the learning due to the high order of the probability distribution (a problem of curse of dimensionality), the probability distribution is factorized and approximated by a set of lower-order probability distributions by applying the idea of relationship decomposition recursively to components and subcomponents. Based on the proposed method, a handwritten Hangul (Korean) character recognition system is developed. Recognition experiments conducted on a public database show the effectiveness of the proposed relationship modeling. The recognition accuracy increased by 5.5 percent in comparison to the most successful system ever reported. View full abstract»

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  • Convergence and application of online active sampling using orthogonal pillar vectors

    Page(s): 1197 - 1207
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (828 KB) |  | HTML iconHTML  

    The analysis of convergence and its application is shown for the Active Sampling-at-the-Boundary method applied to multidimensional space using orthogonal pillar vectors. Active learning method facilitates identifying an optimal decision boundary for pattern classification in machine learning. The result of this method is compared with the standard active learning method that uses random sampling on the decision boundary hyperplane. The comparison is done through simulation and application to the real-world data from the UCI benchmark data set. The boundary is modeled as a nonseparable linear decision hyperplane in multidimensional space with a stochastic oracle. View full abstract»

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  • Tracking multiple humans in complex situations

    Page(s): 1208 - 1221
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1610 KB) |  | HTML iconHTML  

    Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences. View full abstract»

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  • A unified framework for subspace face recognition

    Page(s): 1222 - 1228
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (708 KB) |  | HTML iconHTML  

    PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods. View full abstract»

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  • Extraction of shift invariant wavelet features for classification of images with different sizes

    Page(s): 1228 - 1233
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (734 KB) |  | HTML iconHTML  

    An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods. View full abstract»

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  • Minimal representations of 3D models in terms of image parameters under calibrated and uncalibrated perspective

    Page(s): 1234 - 1238
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (330 KB) |  | HTML iconHTML  

    Indexing is a well-known paradigm for object recognition. In indexing, each 3D model is represented as the set of values assumed by a given vector of image parameters in correspondence to all the possible images of the 3D model. An open problem, posed by Jacobs (1992), concerned the minimum dimensionality of such sets under perspective. This paper proves that, under calibrated or uncalibrated perspective, the minimum dimensionality of the set representing any 3D modeled point-set is two. Two-dimensional representations are found also for 3D curved objects. View full abstract»

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  • A simple technique for improving camera displacement estimation in eye-in-hand visual servoing

    Page(s): 1239 - 1242
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (415 KB) |  | HTML iconHTML  

    A simple technique for estimating the camera displacement from point correspondences in eye-in-hand visual servoing is presented. The idea for providing more accurate results than existing methods consists of taking into account that the point correspondences used during the camera motion correspond to stationary spatial points, hence exploiting additional information, This is done by first estimating the object Euclidean structure and then estimating the camera displacement from this estimate. View full abstract»

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  • Distance-preserving projection of high-dimensional data for nonlinear dimensionality reduction

    Page(s): 1243 - 1246
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    A distance-preserving method is presented to map high-dimensional data sequentially to low-dimensional space. It preserves exact distances of each data point to its nearest neighbor and to some other near neighbors. Intrinsic dimensionality of data is estimated by examining the preservation of interpoint distances. The method has no user-selectable parameter. It can successfully project data when the data points are spread among multiple clusters. Results of experiments show its usefulness in projecting high-dimensional data. View full abstract»

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  • TPAMI Information for authors

    Page(s): c3
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    Freely Available from IEEE
  • [Back cover]

    Page(s): c4
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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

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Editor-in-Chief
David A. Forsyth
University of Illinois