Results 1  10
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105
Enhanced local texture feature sets for face recognition under difficult lighting conditions
 In Proc. AMFG’07
, 2007
"... Abstract. Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. S ..."
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Cited by 274 (10 self)
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Abstract. Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives stateoftheart performance on three popular datasets chosen to test recognition under difficult
Geometric mean for subspace selection
 TIANJIN UNIVERSITY. Downloaded on December 8, 2009 at 04:33 from IEEE Xplore. Restrictions apply. YUAN et al.: BINARY SPARSE NONNEGATIVE MATRIX FACTORIZATION 777
, 2009
"... Abstract—Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher’s linear discriminant analysis (FLDA), which has been successfully employed in many fiel ..."
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Cited by 52 (11 self)
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Abstract—Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher’s linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the KullbackLeibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions. Index Terms—Arithmetic mean, Fisher’s linear discriminant analysis (FLDA), geometric mean, KullbackLeibler (KL) divergence, machine learning, subspace selection (or dimensionality reduction), visualization. Ç 1
Discriminant locally linear embedding with highorder tensor data
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B: CYBERNETICS
, 2008
"... Graphembedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this framework, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local ge ..."
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Cited by 44 (12 self)
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Graphembedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this framework, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local geometric properties within each class are preserved according to the locally linear embedding (LLE) criterion, and the separability between different classes is enforced by maximizing margins between point pairs on different classes. To deal with the outofsample problem in visual recognition with vector input, the linear version of DLLE, i.e., linearization of DLLE (DLLE/L), is directly proposed through the graphembedding framework. Moreover, we propose its multilinear version, i.e., tensorization of DLLE, for the outofsample problem with highorder tensor input. Based on DLLE, a procedure for gait recognition is described. We conduct comprehensive experiments on both gait and face recognition, and observe that: 1) DLLE along its linearization and tensorization outperforms the related versions of linear discriminant analysis, and DLLE/L demonstrates greater effectiveness than the linearization of LLE; 2) algorithms based on tensor representations are generally superior to linear algorithms when dealing with intrinsically highorder data; and 3) for human gait recognition, DLLE/L generally obtains higher accuracy than stateoftheart gait recognition algorithms on the standard University of South Florida gait database.
Patch alignment for dimensionality reduction
 IEEE Trans. Knowl. Data Eng
, 2009
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 37 (12 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Bilinear classifiers for visual recognition
 In IEEE Computer Vision and Pattern Recognition (CVPR
, 2010
"... We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative variant of bilinear models, which capture the dependence of data on multiple factors. Such models are particularly appropriate for visual data that is better represented as a matrix or tensor, rather than ..."
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Cited by 27 (3 self)
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We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative variant of bilinear models, which capture the dependence of data on multiple factors. Such models are particularly appropriate for visual data that is better represented as a matrix or tensor, rather than a vector. Matrix encodings allow for more natural regularization through rank restriction. For example, a rankone restriction produces a bilinear classifier that can be interpreted as a separable filter. We also use bilinear classifiers for transfer learning by sharing linear factors between different tasks. Finally, we show that bilinear classifiers can be trained with biconvex programs. Such programs are optimized with coordinate descent, where each step is equivalent to a standard convex problem. This allows us to leverage existing SVM solvers during learning. We demonstrate bilinear SVMs on difficult problems of people detection in video sequences and action classification of video sequences, achieving stateoftheart results in both. 1
Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition
 IEEE Trans. Neural Netw
, 2009
"... This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensortovector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features direc ..."
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Cited by 20 (12 self)
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This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensortovector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features directly from tensorial data. The smallsamplesize (SSS) problem present when discriminant solutions are applied to the problem of gait recognition is discussed and a regularization procedure is introduced to address it. The effectiveness of the proposed regularization is demonstrated in the experiments and the regularized UMLDA algorithm is shown to outperform other multilinear subspace solutions in gait recognition. 1.
Correlation Metric for Generalized Feature Extraction
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2008
"... Abstract—Beyond conventional linear and kernelbased feature extraction, we present a more generalized formulation for feature extraction in this paper. Two representative algorithms using the correlation metric are proposed based on this formulation. Correlation Embedding Analysis (CEA), which inco ..."
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Cited by 18 (2 self)
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Abstract—Beyond conventional linear and kernelbased feature extraction, we present a more generalized formulation for feature extraction in this paper. Two representative algorithms using the correlation metric are proposed based on this formulation. Correlation Embedding Analysis (CEA), which incorporates both correlational mapping and discriminant analysis, boosts the discriminating power by mapping the data from a highdimensional hypersphere onto another lowdimensional hypersphere and preserving the neighboring relations with localsensitive graph modeling. Correlational Principal Component Analysis (CPCA) generalizes the Principal Component Analysis (PCA) algorithm to the case with data distributed on a highdimensional hypersphere. Their advantages stem from two facts: 1) directly working on normalized data, which are often the outputs from data preprocessing, and 2) directly designed with the correlation metric, which is shown to be generally better than euclidean distance for classification purpose in many realworld applications. Extensive visual recognition experiments compared with existing feature extraction algorithms demonstrate the effectiveness of the proposed algorithms. Index Terms—Feature extraction, graph embedding, correlation embedding analysis, correlational principal component analysis, face recognition. Ç 1
Face and human gait recognition using imagetoclass distance
 IEEE Trans. Circuits Syst. Video Technol
, 2010
"... Abstract—We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set ..."
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Cited by 14 (3 self)
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Abstract—We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set of local features sampled at each pixel. We formulate an integer programming problem to compute the distance (referred to as the imagetoclass distance) from one probe image to all the gallery images belonging to a certain class, in which any feature of the probe image can be matched to only one feature from one of the gallery images. Considering computational efficiency as well as the fact that face images or average human silhouette images are roughly aligned in the preprocessing step, we also enforce a spatial neighborhood constraint by only allowing neighboring features that are within a given spatial distance to be considered for feature matching. The integer programming problem is further treated as a classical minimumweight bipartite graph matching problem, which can be efficiently solved with the Kuhn–Munkres algorithm. We perform comprehensive experiments on three benchmark face databases: 1) the CMU PIE database; 2) the FERET database; and 3) the FRGC database, as well as the USF Human ID gait database. The experiments clearly demonstrate the effectiveness of our imagetoclass distance. Index Terms—Face recognition, human gait recognition, imagetoclass distance. I.
Semisupervised bilinear subspace learning
 IEEE Trans. Image Process
, 2009
"... Abstract—Recent research has demonstrated the success of tensor based subspace learning in both unsupervised and supervised configurations (e.g., 2D PCA, 2D LDA, and DATER). In this correspondence, we present a new semisupervised subspace learning algorithm by integrating the tensor representatio ..."
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Cited by 14 (3 self)
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Abstract—Recent research has demonstrated the success of tensor based subspace learning in both unsupervised and supervised configurations (e.g., 2D PCA, 2D LDA, and DATER). In this correspondence, we present a new semisupervised subspace learning algorithm by integrating the tensor representation and the complementary information conveyed by unlabeled data. Conventional semisupervised algorithms mostly impose a regularization term based on the data representation in the original feature space. Instead, we utilize graph Laplacian regularization based on the lowdimensional feature space. An iterative algorithm, referred to as adaptive regularization based semisupervised discriminant analysis with tensor representation (ARSDA/T), is also developed to compute the solution. In addition to handling tensor data, a vectorbased variant (ARSDA/V) is also presented, in which the tensor data are converted into vectors before subspace learning. Comprehensive experiments on the CMU PIE and YALEB databases demonstrate that ARSDA/T brings significant improvement in face recognition accuracy over both conventional supervised and semisupervised subspace learning algorithms. Index Terms—Adaptive regularization, dimensionality reduction, face recognition, semisupervised learning. I.
Music genre classification using locality preserving nonnegative tensor factorization and sparse representations
 In 10th International Society for Music Information Retrieval Conference (ISMIR
, 2009
"... A robust music genre classification framework is proposed that combines the rich, psychophysiologically grounded properties of auditory cortical representations of music recordings and the power of sparse representationbased classifiers. A novel multilinear subspace analysis method that incorporat ..."
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Cited by 14 (0 self)
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A robust music genre classification framework is proposed that combines the rich, psychophysiologically grounded properties of auditory cortical representations of music recordings and the power of sparse representationbased classifiers. A novel multilinear subspace analysis method that incorporates the underlying geometrical structure of the cortical representations space into nonnegative tensor factorization is proposed for dimensionality reduction compatible to the working principle of sparse representationbased classification. The proposed method is referred to as Locality Preserving NonNegative Tensor Factorization (LPNTF). Dimensionality reduction is shown to play a crucial role within the classification framework under study. Music genre classification accuracy of 92.4 % and 94.38% on the GTZAN and the ISMIR2004 Genre datasets is reported, respectively. Both accuracies outperform any accuracy ever reported for state of the art music genre classification algorithms applied to the aforementioned datasets. 1.