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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date June 2013

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  • [Front cover]

    Publication Year: 2013 , Page(s): c1
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  • IEEE Transactions on Pattern Analysis and Machine Intelligence - Editorial board

    Publication Year: 2013 , Page(s): c2
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  • Editor's Note

    Publication Year: 2013 , Page(s): 1281 - 1283
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  • Discovering Low-Rank Shared Concept Space for Adapting Text Mining Models

    Publication Year: 2013 , Page(s): 1284 - 1297
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1748 KB) |  | HTML iconHTML  

    We propose a framework for adapting text mining models that discovers low-rank shared concept space. Our major characteristic of this concept space is that it explicitly minimizes the distribution gap between the source domain with sufficient labeled data and the target domain with only unlabeled data, while at the same time it minimizes the empirical loss on the labeled data in the source domain.... View full abstract»

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  • Numerical Conditioning Problems and Solutions for Nonparametric i.i.d. Statistical Active Contours

    Publication Year: 2013 , Page(s): 1298 - 1311
    Cited by:  Papers (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1474 KB) |  | HTML iconHTML  

    In this paper, we propose an active contour model based on nonparametric independent and identically distributed (i.i.d.) statistics of the image that can segment an image without any a priori information about the intensity distributions of the region of interest or the background. This is not, however, the first active contour model proposed to solve the segmentation problem under these same ass... View full abstract»

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  • A Game-Theoretic Approach to Hypergraph Clustering

    Publication Year: 2013 , Page(s): 1312 - 1327
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1196 KB) |  | HTML iconHTML  

    Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. In this paper, we offer a radica... View full abstract»

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  • A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

    Publication Year: 2013 , Page(s): 1328 - 1342
    Cited by:  Papers (2)
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    Request Permissions | Click to expandAbstract | PDF file iconPDF (1451 KB) |  | HTML iconHTML  

    Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation... View full abstract»

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  • Bayesian Estimation of Turbulent Motion

    Publication Year: 2013 , Page(s): 1343 - 1356
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    Request Permissions | Click to expandAbstract | PDF file iconPDF (2184 KB) |  | HTML iconHTML  

    Based on physical laws describing the multiscale structure of turbulent flows, this paper proposes a regularizer for fluid motion estimation from an image sequence. Regularization is achieved by imposing some scale invariance property between histograms of motion increments computed at different scales. By reformulating this problem from a Bayesian perspective, an algorithm is proposed to jointly ... View full abstract»

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  • Coupled Gaussian processes for pose-invariant facial expression recognition

    Publication Year: 2013 , Page(s): 1357 - 1369
    Cited by:  Papers (6)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1934 KB) |  | HTML iconHTML  

    We propose a method for head-pose invariant facial expression recognition that is based on a set of characteristic facial points. To achieve head-pose invariance, we propose the Coupled Scaled Gaussian Process Regression (CSGPR) model for head-pose normalization. In this model, we first learn independently the mappings between the facial points in each pair of (discrete) nonfrontal poses and the f... View full abstract»

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  • Efficient Optimization of Performance Measures by Classifier Adaptation

    Publication Year: 2013 , Page(s): 1370 - 1382
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2227 KB) |  | HTML iconHTML  

    In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing ... View full abstract»

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  • Fourier Lucas-Kanade Algorithm

    Publication Year: 2013 , Page(s): 1383 - 1396
    Cited by:  Papers (7)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2907 KB) |  | HTML iconHTML  

    In this paper, we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas & Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is adva... View full abstract»

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  • Guided Image Filtering

    Publication Year: 2013 , Page(s): 1397 - 1409
    Cited by:  Papers (63)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (8347 KB) |  | HTML iconHTML  

    In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better... View full abstract»

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  • Heterogeneous Face Recognition Using Kernel Prototype Similarities

    Publication Year: 2013 , Page(s): 1410 - 1422
    Cited by:  Papers (12)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (5372 KB) |  | HTML iconHTML  

    Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e.g., forensics and surveillance), where the gallery databases are populated with photographs (e.g., mug shot or passport photographs) but the probe images ar... View full abstract»

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  • Local Transform Features and Hybridization for Accurate Face and Human Detection

    Publication Year: 2013 , Page(s): 1423 - 1436
    Cited by:  Papers (3)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2210 KB) |  | HTML iconHTML  

    We propose two novel local transform features: local gradient patterns (LGP) and binary histograms of oriented gradients (BHOG). LGP assigns one if the neighboring gradient of a given pixel is greater than its average of eight neighboring gradients and zero otherwise, which makes the local intensity variations along the edge components robust. BHOG assigns one if the histogram bin has a higher val... View full abstract»

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  • Locally Orderless Registration

    Publication Year: 2013 , Page(s): 1437 - 1450
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2098 KB) |  | HTML iconHTML  

    This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. ... View full abstract»

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  • Monocular SLAM with Conditionally Independent Split Mapping

    Publication Year: 2013 , Page(s): 1451 - 1463
    Request Permissions | Click to expandAbstract | PDF file iconPDF (2724 KB) |  | HTML iconHTML  

    The recovery of structure from motion in real time over extended areas demands methods that mitigate the effects of computational complexity and arithmetical inconsistency. In this paper, we develop SCISM, an algorithm based on relative frame bundle adjustment, which splits the recovered map of 3D landmarks and keyframes poses so that the camera can continue to grow and explore a local map in real... View full abstract»

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  • Robust Extrema Features for Time-Series Data Analysis

    Publication Year: 2013 , Page(s): 1464 - 1479
    Cited by:  Papers (1)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (3731 KB) |  | HTML iconHTML  

    The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-seri... View full abstract»

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  • Single-Image Vignetting Correction from Gradient Distribution Symmetries

    Publication Year: 2013 , Page(s): 1480 - 1494
    Request Permissions | Click to expandAbstract | PDF file iconPDF (4134 KB) |  | HTML iconHTML  

    We present novel techniques for single-image vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the presumed optical center and passing through the pixel. An RG is an image gradient along the radial ... View full abstract»

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  • Surface and Curve Skeletonization of Large 3D Models on the GPU

    Publication Year: 2013 , Page(s): 1495 - 1508
    Cited by:  Papers (2)
    Request Permissions | Click to expandAbstract | PDF file iconPDF (6369 KB) |  | HTML iconHTML  

    We present a GPU-based framework for extracting surface and curve skeletons of 3D shapes represented as large polygonal meshes. We use an efficient parallel search strategy to compute point-cloud skeletons and their distance and feature transforms (FTs) with user-defined precision. We regularize skeletons by a new GPU-based geodesic tracing technique which is orders of magnitude faster and more ac... View full abstract»

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  • The impact of cluster representatives on the convergence of the K-modes type clustering

    Publication Year: 2013 , Page(s): 1509 - 1522
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1141 KB) |  | HTML iconHTML  

    As a leading partitional clustering technique, $(k)$-modes is one of the most computationally efficient clustering methods for categorical data. In the $(k)$-modes, a cluster is represented by a “mode,” which is composed of the attribute value that occurs most frequently in each attribute domain of the cluster, whereas, in real applications, using only one attribute value in ... View full abstract»

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  • The Infinite-Order Conditional Random Field Model for Sequential Data Modeling

    Publication Year: 2013 , Page(s): 1523 - 1534
    Request Permissions | Click to expandAbstract | PDF file iconPDF (1057 KB) |  | HTML iconHTML  

    Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful... View full abstract»

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  • New Transactions Newsletter

    Publication Year: 2013 , Page(s): 1535
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  • IEEE Open Access Publishing

    Publication Year: 2013 , Page(s): 1536
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  • [Back inside cover]

    Publication Year: 2013 , Page(s): c3
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  • [Back cover]

    Publication Year: 2013 , 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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
David A. Forsyth
University of Illinois
e-mail: daf@illinois.edu