Results 1 - 10 of 397

two-dimensional

by Babiano, Thomas Dubos , 2004
"... On the contribution of coherent vortices to the ..."
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On the contribution of coherent vortices to the

Discriminant analysis with tensor representation

by Shuicheng Yan, Dong Xu, Qiang Yang, Lei Zhang, Xiaoou Tang, Hong-jiang Zhang - in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2005 , 2005
"... In this paper, we present a novel approach to solving the supervised dimensionality reduction problem by encoding an image object as a general tensor of 2nd or higher order. First, we propose a Discriminant Tensor Criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspa ..."
Abstract - Cited by 53 (13 self) - Add to MetaCart
subspaces are derived for feature selection. Then, a novel approach called k-mode Cluster-based Discriminant Analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions. We call this algorithm Discriminant Analysis with Tensor Representation (DATER

A comparison of classifiers and document representations for the routing problem

by Hinrich Schütze, David A. Hull, Jan O. Pedersen - ANNUAL ACM CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL - ACM SIGIR , 1995
"... In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization: linear discriminant ..."
Abstract - Cited by 196 (2 self) - Add to MetaCart
discriminant analysis, logistic regression, and neural networks. We demonstrate that the classifiers perform 1015 % better than relevance feedback via Rocchio expansion for the TREC-2 and TREC-3 routing tasks. Error minimization is difficult in high-dimensional feature spaces because the convergence process

General tensor discriminant analysis and Gabor featuresforgaitrecognition,”IEEE Trans

by Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank - Pattern Anal. Mach. Intell , 2007
"... Abstract — The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by ..."
Abstract - Cited by 105 (11 self) - Add to MetaCart
by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

by Tae-kyun Kim, Josef Kittler, Roberto Cipolla - IEEE Trans. Pattern Analysis and Machine Intelligence , 2007
"... Abstract—We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object’s appearance due to changing camera pose and lighting conditions. Canonical Correlations (also known as principal or canonical angles), which can be thought of as ..."
Abstract - Cited by 130 (11 self) - Add to MetaCart
of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired

R.: Tensor Canonical Correlation Analysis for Action Classification

by Tae-kyun Kim, Shu-fai Wong, Roberto Cipolla - In: CVPR (2007 , 2007
"... We introduce a new framework, namely Tensor Canonical Correlation Analysis (TCCA) which is an extension of classical Canonical Correlation Analysis (CCA) to multidimensional data arrays (or tensors) and apply this for action/gesture classification in videos. By Tensor CCA, joint space-time linear re ..."
Abstract - Cited by 76 (6 self) - Add to MetaCart
We introduce a new framework, namely Tensor Canonical Correlation Analysis (TCCA) which is an extension of classical Canonical Correlation Analysis (CCA) to multidimensional data arrays (or tensors) and apply this for action/gesture classification in videos. By Tensor CCA, joint space-time linear

Tensor subspace analysis

by Xiaofei He, Deng Cai, Partha Niyogi - In Advances in Neural Information Processing Systems 18 (NIPS , 2005
"... Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear subspace learning algorithms include Principal Component Analysis (PCA), Linear Discriminant An ..."
Abstract - Cited by 65 (4 self) - Add to MetaCart
by learning a lower dimensional tensor subspace. We compare our proposed approach with PCA, LDA and LPP methods on two standard databases. Experimental results demonstrate that TSA achieves better recognition rate, while being much more efficient. 1

Subspace learning based on tensor analysis

by Deng Cai, Xiaofei He, Jiawei Han, Deng Cai, Xiaofei He, Jiawei Han , 2005
"... Linear dimensionality reduction techniques have been widely used in pattern recognition and computer vision, such as face recognition, image retrieval, etc. The typical methods include Principal Component Analysis (PCA) which is unsupervised and Linear Discriminant Analysis (LDA) which is supervised ..."
Abstract - Cited by 15 (4 self) - Add to MetaCart
is supervised. Both of them consider an m1 × m2 image as a high dimensional vector in Rm1×m2. Such a vector representation fails to take into account the spatial locality of pixels in the image. An image is intrinsically a matrix. In this paper, we consider an image as the second order tensor in Rm1⊗Rm2

Coil sensitivity encoding for fast MRI. In:

by Klaas P Pruessmann , Markus Weiger , Markus B Scheidegger , Peter Boesiger - Proceedings of the ISMRM 6th Annual Meeting, , 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
Abstract - Cited by 193 (3 self) - Add to MetaCart
of this section gives a practical description of the Cartesian case. The following parts are dedicated to general theory, SNR and error considerations, and sensitivity assessment. Sensitivity Encoding With Cartesian Sampling of k-Space In two-dimensional (2D) Fourier imaging with common Cartesian sampling of k

Image Clustering with Tensor Representation

by Xiaofei He, Deng Cai, Haifeng Liu, Jiawei Han , 2005
"... We consider the problem of image representation and clustering. Traditionally, an n1 × n2 image is represented by a vector in the Euclidean space R n1×n2. Some learning algorithms are then applied to these vectors in such a high dimensional space for dimensionality reduction, classification, and clu ..."
Abstract - Cited by 10 (0 self) - Add to MetaCart
We consider the problem of image representation and clustering. Traditionally, an n1 × n2 image is represented by a vector in the Euclidean space R n1×n2. Some learning algorithms are then applied to these vectors in such a high dimensional space for dimensionality reduction, classification
Results 1 - 10 of 397