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		<title><![CDATA[ Pattern Analysis and Machine Intelligence, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 34 </description>
		<year>2013</year>
		<month>May      </month>
		<day>16</day>
		<item>
			<title><![CDATA[Cover1]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516866]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516866]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>c1</startPage>
			<endPage>c1</endPage>
			<fileSize>217</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover2]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516860]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516860]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>c2</startPage>
			<endPage>c2</endPage>
			<fileSize>201</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341752]]></link>
			<description><![CDATA[This manuscript considers the following &amp;#x201C;graph classification&amp;#x201D; question: Given a collection of graphs and associated classes, how can one predict the class of a newly observed graph? To address this question, we propose a statistical model for graph/class pairs. This model naturally leads to a set of estimators to identify the class-conditional signal, or &amp;#x201C;signal-subgraph,&amp;#x201D; defined as the collection of edges that are probabilistically different between the classes. The estimators admit classifiers which are asymptotically optimal and efficient, but which differ by their assumption about the &amp;#x201C;coherency&amp;#x201D; of the signal-subgraph (coherency is the extent to which the signal-edges &amp;#x201C;stick together&amp;#x201D; around a common subset of vertices). Via simulation, the best estimator is shown to be not just a function of the coherency of the model, but also the number of training samples. These estimators are employed to address a contemporary neuroscience question: Can we classify &amp;#x201C;connectomes&amp;#x201D; (brain-graphs) according to sex? The answer is yes, and significantly better than all benchmark algorithms considered. Synthetic data analysis demonstrates that even when the model is correct, given the relatively small number of training samples, the estimated signal-subgraph should be taken with a grain of salt. We conclude by discussing several possible extensions.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341752]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1539</startPage>
			<endPage>1551</endPage>
			<fileSize>993</fileSize>
			<authors><![CDATA[Vogelstein, Joshua T.;Gray Roncal, William;Vogelstein, R.Jacob;Priebe, Carey E.;]]></authors>
		</item>
		<item>
			<title><![CDATA[3D Facial Landmark Detection under Large Yaw and Expression Variations]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361404]]></link>
			<description><![CDATA[A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361404]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1552</startPage>
			<endPage>1564</endPage>
			<fileSize>1933</fileSize>
			<authors><![CDATA[Perakis, Panagiotis;Passalis, Georgios;Theoharis, Theoharis;Kakadiaris, Ioannis A.;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Branch-and-Bound Approach to Correspondence and Grouping Problems]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6389676]]></link>
			<description><![CDATA[Data correspondence/grouping under an unknown parametric model is a fundamental topic in computer vision. Finding feature correspondences between two images is probably the most popular application of this research field, and is the main motivation of our work. It is a key ingredient for a wide range of vision tasks, including three-dimensional reconstruction and object recognition. Existing feature correspondence methods are based on either local appearance similarity or global geometric consistency or a combination of both in some heuristic manner. None of these methods is fully satisfactory, especially in the presence of repetitive image textures or mismatches. In this paper, we present a new algorithm that combines the benefits of both appearance-based and geometry-based methods and mathematically guarantees a global optimization. Our algorithm accepts the two sets of features extracted from two images as input, and outputs the feature correspondences with the largest number of inliers, which verify both the appearance similarity and geometric constraints. Specifically, we formulate the problem as a mixed integer program and solve it efficiently by a series of linear programs via a branch-and-bound procedure. We subsequently generalize our framework in the context of data correspondence/grouping under an unknown parametric model and show it can be applied to certain classes of computer vision problems. Our algorithm has been validated successfully on synthesized data and challenging real images.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6389676]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1565</startPage>
			<endPage>1576</endPage>
			<fileSize>1912</fileSize>
			<authors><![CDATA[Bazin, Jean-Charles;Li, Hongdong;Kweon, In So;Demonceaux, C&#x00E9;dric;Vasseur, Pascal;Ikeuchi, Katsushi;]]></authors>
		</item>
		<item>
			<title><![CDATA[A General Framework for Tracking Multiple People from a Moving Camera]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361406]]></link>
			<description><![CDATA[In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. To determine all of the trajectories robustly and in a 3D coordinate system, we estimate both the camera's ego-motion and the people's paths within a single coherent framework. The tracking problem is framed as finding the MAP solution of a posterior probability, and is solved using the reversible jump Markov chain Monte Carlo (RJ-MCMC) particle filtering method. We evaluate our system on challenging datasets taken from moving cameras, including an outdoor street scene video dataset, as well as an indoor RGB-D dataset collected in an office. Experimental evidence shows that the proposed method can robustly estimate a camera's motion from dynamic scenes and stably track people who are moving independently or interacting.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361406]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1577</startPage>
			<endPage>1591</endPage>
			<fileSize>4346</fileSize>
			<authors><![CDATA[Choi, Wongun;Pantofaru, Caroline;Savarese, Silvio;]]></authors>
		</item>
		<item>
			<title><![CDATA[Automatic Relevance Determination in Nonnegative Matrix Factorization with the $(beta)$-Divergence]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341758]]></link>
			<description><![CDATA[This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the $(beta)$--divergence. The $(beta)$-divergence is a family of cost functions that includes the squared euclidean distance, Kullback-Leibler (KL) and Itakura-Saito (IS) divergences as special cases. Learning the model order is important as it is necessary to strike the right balance between data fidelity and overfitting. We propose a Bayesian model based on automatic relevance determination (ARD) in which the columns of the dictionary matrix and the rows of the activation matrix are tied together through a common scale parameter in their prior. A family of majorization-minimization (MM) algorithms is proposed for maximum a posteriori (MAP) estimation. A subset of scale parameters is driven to a small lower bound in the course of inference, with the effect of pruning the corresponding spurious components. We demonstrate the efficacy and robustness of our algorithms by performing extensive experiments on synthetic data, the swimmer dataset, a music decomposition example, and a stock price prediction task.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341758]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1592</startPage>
			<endPage>1605</endPage>
			<fileSize>1920</fileSize>
			<authors><![CDATA[Tan, Vincent Y.F.;F&#x00E9;votte, C&#x00E9;dric;]]></authors>
		</item>
		<item>
			<title><![CDATA[Clustering Dynamic Textures with the Hierarchical EM Algorithm for Modeling Video]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341753]]></link>
			<description><![CDATA[Dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and video registration. In this paper, we derive a new algorithm for clustering DT models that is based on the hierarchical EM algorithm. The proposed clustering algorithm is capable of both clustering DTs and learning novel DT cluster centers that are representative of the cluster members in a manner that is consistent with the underlying generative probabilistic model of the DT. We also derive an efficient recursive algorithm for sensitivity analysis of the discrete-time Kalman smoothing filter, which is used as the basis for computing expectations in the E-step of the HEM algorithm. Finally, we demonstrate the efficacy of the clustering algorithm on several applications in motion analysis, including hierarchical motion clustering, semantic motion annotation, and learning bag-of-systems (BoS) codebooks for dynamic texture recognition.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341753]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1606</startPage>
			<endPage>1621</endPage>
			<fileSize>3915</fileSize>
			<authors><![CDATA[Mumtaz, Adeel;Coviello, Emanuele;Lanckriet, Gert R.G.;Chan, Antoni B.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Color Invariants for Person Reidentification]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6357194]]></link>
			<description><![CDATA[We revisit the problem of specific object recognition using color distributions. In some applications&amp;#x2014;such as specific person identification&amp;#x2014;it is highly likely that the color distributions will be multimodal and hence contain a special structure. Although the color distribution changes under different lighting conditions, some aspects of its structure turn out to be invariants. We refer to this structure as an intradistribution structure, and show that it is invariant under a wide range of imaging conditions while being discriminative enough to be practical. Our signature uses shape context descriptors to represent the intradistribution structure. Assuming the widely used diagonal model, we validate that our signature is invariant under certain illumination changes. Experimentally, we use color information as the only cue to obtain good recognition performance on publicly available databases covering both indoor and outdoor conditions. Combining our approach with the complementary covariance descriptor, we demonstrate results exceeding the state-of-the-art performance on the challenging VIPeR and CAVIAR4REID databases.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6357194]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1622</startPage>
			<endPage>1634</endPage>
			<fileSize>2147</fileSize>
			<authors><![CDATA[Kviatkovsky, Igor;Adam, Amit;Rivlin, Ehud;]]></authors>
		</item>
		<item>
			<title><![CDATA[Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365192]]></link>
			<description><![CDATA[This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365192]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1635</startPage>
			<endPage>1648</endPage>
			<fileSize>3151</fileSize>
			<authors><![CDATA[Yang, Yang;Saleemi, Imran;Shah, Mubarak;]]></authors>
		</item>
		<item>
			<title><![CDATA[Hierarchical Object Parsing from Structured Noisy Point Clouds]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381418]]></link>
			<description><![CDATA[Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as active shape and active appearance models (AAMs) lack the necessary flexibility for this task, while recent approaches such as the recursive compositional models make model simplifications to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer which is a deformation of a hidden principal component analysis (PCA) shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state-of-the-art parsing errors on two standard datasets without using any intensity information.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381418]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1649</startPage>
			<endPage>1659</endPage>
			<fileSize>1406</fileSize>
			<authors><![CDATA[Barbu, Adrian;]]></authors>
		</item>
		<item>
			<title><![CDATA[Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365194]]></link>
			<description><![CDATA[A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) $(underline{bf Y})$ from a tensor $(underline{bf X})$ through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both $(underline{bf X})$ and $(underline{bf Y})$. Instead of decomposing $(underline{bf X})$ and $(underline{bf Y})$ individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365194]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1660</startPage>
			<endPage>1673</endPage>
			<fileSize>2065</fileSize>
			<authors><![CDATA[Zhao, Qibin;Caiafa, Cesar F.;Mandic, Danilo P.;Chao, Zenas C.;Nagasaka, Yasuo;Fujii, Naotaka;Zhang, Liqing;Cichocki, Andrzej;]]></authors>
		</item>
		<item>
			<title><![CDATA[Joint Albedo Estimation and Pose Tracking from Video]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361408]]></link>
			<description><![CDATA[The albedo of a Lambertian object is a surface property that contributes to an object's appearance under changing illumination. As a signature independent of illumination, the albedo is useful for object recognition. Single image-based albedo estimation algorithms suffer due to shadows and non-Lambertian effects of the image. In this paper, we propose a sequential algorithm to estimate the albedo from a sequence of images of a known 3D object in varying poses and illumination conditions. We first show that by knowing/estimating the pose of the object at each frame of a sequence, the object's albedo can be efficiently estimated using a Kalman filter. We then extend this for the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through a Rao-Blackwellized particle filter (RBPF). More specifically, the albedo is marginalized from the posterior distribution and estimated analytically using the Kalman filter, while the pose parameters are estimated using importance sampling and by minimizing the projection error of the face onto its spherical harmonic subspace, which results in an illumination-insensitive pose tracking algorithm. Illustrations and experiments are provided to validate the effectiveness of the approach using various synthetic and real sequences followed by applications to unconstrained, video-based face recognition.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361408]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1674</startPage>
			<endPage>1689</endPage>
			<fileSize>2751</fileSize>
			<authors><![CDATA[Taheri, Sima;Sankaranarayanan, Aswin C.;Chellappa, Rama;]]></authors>
		</item>
		<item>
			<title><![CDATA[Learning Full Pairwise Affinities for Spectral Segmentation]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341755]]></link>
			<description><![CDATA[Segmenting a single image into multiple coherent groups remains a challenging task in the field of computer vision. Particularly, spectral segmentation which uses the global information embedded in the spectrum of a given image's affinity matrix is a major trend in image segmentation. This paper focuses on the problem of efficiently learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. We first construct a sparse multilayer graph whose nodes are both the pixels and the oversegmented regions obtained by an unsupervised segmentation algorithm. By applying the semi-supervised learning strategy to this graph, the intra and interlayer affinities between all pairs of nodes can be estimated without iteration. These pairwise affinities are then applied into the spectral segmentation algorithms. In this paper, two types of spectral segmentation algorithms are introduced: $(K)$-way segmentation and hierarchical segmentation. Our algorithms provide high-quality segmentations which preserve object details by directly incorporating the full-range connections. Moreover, since our full affinity matrix is defined by the inverse of a sparse matrix, its eigendecomposition can be efficiently computed. The experimental results on the BSDS and MSRC image databases demonstrate the superiority of our segmentation algorithms in terms of relevance and accuracy compared with existing popular methods.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341755]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1690</startPage>
			<endPage>1703</endPage>
			<fileSize>5467</fileSize>
			<authors><![CDATA[Kim, Tae Hoon;Lee, Kyoung Mu;Lee, Sang Uk;]]></authors>
		</item>
		<item>
			<title><![CDATA[Learning to Track and Identify Players from Broadcast Sports Videos]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516867]]></link>
			<description><![CDATA[Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516867]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1704</startPage>
			<endPage>1716</endPage>
			<fileSize>1745</fileSize>
			<authors><![CDATA[Lu, Wei-Lwun;Ting, Jo-Anne;Little, James J.;Murphy, Kevin P.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Low-Rank Matrix Approximation with Manifold Regularization]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6399475]]></link>
			<description><![CDATA[This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6399475]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1717</startPage>
			<endPage>1729</endPage>
			<fileSize>1120</fileSize>
			<authors><![CDATA[Zhang, Zhenyue;Zhao, Keke;]]></authors>
		</item>
		<item>
			<title><![CDATA[Nonparametric Illumination Correction for Scanned Document Images via Convex Hulls]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361405]]></link>
			<description><![CDATA[A scanned image of an opened book page often suffers from various scanning artifacts known as scanning shading and dark borders noises. These artifacts will degrade the qualities of the scanned images and cause many problems to the subsequent process of document image analysis. In this paper, we propose an effective method to rectify these scanning artifacts. Our method comes from two observations: that the shading surface of most scanned book pages is quasi-concave and that the document contents are usually printed on a sheet of plain and bright paper. Based on these observations, a shading image can be accurately extracted via convex hulls-based image reconstruction. The proposed method proves to be surprisingly effective for image shading correction and dark borders removal. It can restore a desired shading-free image and meanwhile yield an illumination surface of high quality. More importantly, the proposed method is nonparametric and thus does not involve any user interactions or parameter fine-tuning. This would make it very appealing to nonexpert users in applications. Extensive experiments based on synthetic and real-scanned document images demonstrate the efficiency of the proposed method.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361405]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1730</startPage>
			<endPage>1743</endPage>
			<fileSize>14429</fileSize>
			<authors><![CDATA[Meng, Gaofeng;Xiang, Shiming;Zheng, Nanning;Pan, Chunhong;]]></authors>
		</item>
		<item>
			<title><![CDATA[Parsing Facades with Shape Grammars and Reinforcement Learning]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361407]]></link>
			<description><![CDATA[In this paper, we use shape grammars (SGs) for facade parsing, which amounts to segmenting 2D building facades into balconies, walls, windows, and doors in an architecturally meaningful manner. The main thrust of our work is the introduction of reinforcement learning (RL) techniques to deal with the computational complexity of the problem. RL provides us with techniques such as Q-learning and state aggregation which we exploit to efficiently solve facade parsing. We initially phrase the 1D parsing problem in terms of a Markov Decision Process, paving the way for the application of RL-based tools. We then develop novel techniques for the 2D shape parsing problem that take into account the specificities of the facade parsing problem. Specifically, we use state aggregation to enforce the symmetry of facade floors and demonstrate how to use RL to exploit bottom-up, image-based guidance during optimization. We provide systematic results on the Paris building dataset and obtain state-of-the-art results in a fraction of the time required by previous methods. We validate our method under diverse imaging conditions and make our software and results available online.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6361407]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1744</startPage>
			<endPage>1756</endPage>
			<fileSize>1476</fileSize>
			<authors><![CDATA[Teboul, Olivier;Kokkinos, Iasonas;Simon, Loic;Koutsourakis, Panagiotis;Paragios, Nikos;]]></authors>
		</item>
		<item>
			<title><![CDATA[Toward Open Set Recognition]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365193]]></link>
			<description><![CDATA[To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of &amp;#x201C;closed set&amp;#x201D; recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is &amp;#x201C;open set&amp;#x201D; recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel &amp;#x201C;1-vs-set machine,&amp;#x201D; which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6365193]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1757</startPage>
			<endPage>1772</endPage>
			<fileSize>1726</fileSize>
			<authors><![CDATA[Scheirer, Walter J.;de Rezende Rocha, Anderson;Sapkota, Archana;Boult, Terrance E.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Writer Adaptation with Style Transfer Mapping]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341757]]></link>
			<description><![CDATA[Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes. In this paper, we combine STM with the state-of-the-art classifiers for large-category Chinese handwriting recognition: learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF). Experiments on the online Chinese handwriting database CASIA-OLHWDB demonstrate that STM-based adaptation is very efficient and effective in improving classification accuracy. Semi-supervised adaptation achieves the best performance, while unsupervised adaptation is even better than supervised adaptation. On handwritten text data, semi-supervised adaptation achieves error reduction rates 31.95 and 25.00 percent by LVQ and MQDF, respectively.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6341757]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1773</startPage>
			<endPage>1787</endPage>
			<fileSize>2685</fileSize>
			<authors><![CDATA[Zhang, Xu-Yao;Liu, Cheng-Lin;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461886]]></link>
			<description><![CDATA[In this paper, we present a scalable and exact solution for probabilistic linear discriminant analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the-art performance for both face and speaker recognition. However, it has one major drawback: At training time estimating the latent variables requires the inversion and storage of a matrix whose size grows quadratically with the number of samples for the identity (class). To date, two approaches have been taken to deal with this problem, to 1) use an exact solution that calculates this large matrix and is obviously not scalable with the number of samples or 2) derive a variational approximation to the problem. We present a scalable derivation which is theoretically equivalent to the previous nonscalable solution and thus obviates the need for a variational approximation. Experimentally, we demonstrate the efficacy of our approach in two ways. First, on labeled faces in the wild, we illustrate the equivalence of our scalable implementation with previously published work. Second, on the large Multi-PIE database, we illustrate the gain in performance when using more training samples per identity (class), which is made possible by the proposed scalable formulation of PLDA.]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461886]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>1788</startPage>
			<endPage>1794</endPage>
			<fileSize>513</fileSize>
			<authors><![CDATA[El Shafey, Laurent;McCool, Chris;Wallace, Roy;Marcel, S&#x00E9;bastien;]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover3]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516862]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516862]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>c3</startPage>
			<endPage>c3</endPage>
			<fileSize>202</fileSize>
			<authors><![CDATA[]]></authors>
		</item>
		<item>
			<title><![CDATA[Cover4]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516861]]></link>
			<description><![CDATA[ ]]></description>
			<pubDate><![CDATA[July  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6516861]]></guid>
			<volume>35</volume>
			<issue>7</issue>
			<startPage>c4</startPage>
			<endPage>c4</endPage>
			<fileSize>217</fileSize>
			<authors><![CDATA[]]></authors>
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