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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

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Semi-Supervised Discriminant Analysis using Robust Path-Based Similarity

Semi-Supervised Discriminant Analysis using Robust Path-Base...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Yu Zhang Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality reduction technique in pattern recognition and machine learning. In real-world applications when labeled data are limited, LDA does not work well. Under many situations, however, it is easy to obtain unlabeled data in large quantities. In this paper, we propose a novel dimensionality reduction method, called Semi-Supervised Discriminant Analysis (SSDA), which can utilize both labeled and unlabeled data to perform dimensionality reduction in the semi-supervised setting. Our method uses a robust path-based similarity measure to capture the manifold structure of the data and then uses the obtained similarity to maximize the separability between different classes. A kernel extension of the proposed method for nonlinear dimensionality reduction in the semi-supervised setting is also presented. Experiments on face recognition demonstrate the effectiveness of the proposed method.

关键词： Linear discriminant analysis Scattering Semisupervised learning Machine learning Matrices pattern recognition Face recognition Null space Noise robustness pattern analysis

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Incremental Learning of Nonparametric Bayesian Mixture Models

Incremental Learning of Nonparametric Bayesian Mixture Model...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Ryan Gomes Max Welling Pietro Perona Department of Computation and Neural Systems California Institute of Technology Pasadena CA USA Department of Information and Computer Science University of California Irvine Irvine CA USA

Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here we explore incremental clustering, in which data can arrive continuously. We present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which we call Memory Bounded Variational Dirichlet Process (MB-VDP). the number of clusters are determined flexibly by the data and the approach can be used to automatically discover object categories. the computational requirements required to produce model updates are bounded and do not grow with the amount of data processed. the technique is well suited to very large datasets, and we show that our approach outperforms existing online alternatives for learning nonparametric Bayesian mixture models.

关键词： Bayesian methods Clustering algorithms Training data computer vision computer science Application software Humans Machine vision Dictionaries Image recognition

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Fully Automatic Feature Localization for Medical Images using a Global Vector Concentration Approach

Fully Automatic Feature Localization for Medical Images usin...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Tatsuo Kozakaya Tomoyuki Shibata Tomoyuki Takeguchi Masahide Nishiura Corporate Research and Development Center Toshiba Corporation Kawasaki Japan

In this paper, we propose a novel feature localization method based on a global vector concentration approach. Our approach does not rely on the detection of local salient features around feature points. Instead, we exploit global structural information of the object extracted by calculating the concentration of directional vectors from sampling points. those vectors are combined with local pattern descriptors of a query image and selected from preliminarily trained extended templates by nearest neighbor search. Due to the insensitivity of local changes, our method can handle partially occluded and noisy objects. We apply the proposed method to fully automatic feature localization of the left ventricular in echocardiograms. the results show the effectiveness of our method in comparison with a conventional edge-based method in terms of accuracy and robustness.

关键词： Biomedical imaging Data mining Image sampling Voting Nearest neighbor searches Robustness Object detection computer vision Active contours Shape

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Sequential Sparsification for Change Detection

Sequential Sparsification for Change Detection

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Necmiye Ozay Mario Sznaier Octavia I. Camps Department of Electrical and Computer Engineering Northeastern University Boston MA USA

this paper presents a general method for segmenting a vector valued sequence into an unknown number of sub-sequences where all data points from a subsequence can be represented with the same affine parametric model. the idea is to cluster the data into the minimum number of such subsequences which, as we show, can be cast as a sparse signal recovery problem by exploiting the temporal correlation between consecutive data points. We try to maximize the sparsity (i.e. the number of zero elements) of the first order differences of the sequence of parameter vectors. Each non-zero element in the first order difference sequence corresponds to a change. A weighted l{sub}1 norm based convex approximation is adopted to solve the change detection problem. We apply the proposed method to video segmentation and temporal segmentation of dynamic textures.

关键词： Image segmentation Switches Noise robustness Change detection algorithms computer vision Image edge detection Data engineering Parametric statistics Noise level Pixel

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Action recognition using Exemplar-based Embedding

Action Recognition using Exemplar-based Embedding

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Daniel Weinland Edmond Boyer LJK INRIA Rhone-Alpes France

In this paper, we address the problem of representing human actions using visual cues for the purpose of learning and recognition. Traditional approaches model actions as space-time representations which explicitly or implicitly encode the dynamics of an action through temporal dependencies. In contrast, we propose a new compact and efficient representation which does not account for such dependencies. Instead, motion sequences are represented with respect to a set of discriminative static key-pose exemplars and without modeling any temporal ordering. the interest is a time-invariant representation that drastically simplifies learning and recognition by removing time related information such as speed or length of an action. the proposed representation is equivalent to embedding actions into a space defined by distances to key-pose exemplars. We show how to build such embedding spaces of low dimension by identifying a vocabulary of highly discriminative exemplars using a forward selection. To test our representation, we have used a publicly available dataset which demonstrates that our method can precisely recognize actions, even with cluttered and non-segmented sequences.

关键词： Hidden Markov models Vocabulary Testing computer vision Application software Surveillance Human computer interaction Shape Psychology Image recognition

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Inverse-Polar Ray Projection for Recovering Projective Transformations

Inverse-Polar Ray Projection for Recovering Projective Trans...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Yun Zhang Henry Chu Center for Advanced Computer Studies University of Louisiana Lafayette USA

A ray projection in the inverse-polar space is proposed for recovering a projective transformation between two segmented images. the images are converted from their original Cartesian space to the inverse-polar space. then, the two ray projections - one shift-invariant and the other shift-sensitive - of the inverse-polar images are computed to create two sets of data. Based on the obtained projection data, a two-step strategy is employed to recover the projective transformation. In the first step, the shift-invariant data are used to recover the four affine parameters. In the second step, the shift-sensitive data are used to recover the two projective parameters. the remaining two translation-related parameters are recovered in, e.g., an exhaustive search combined with the two-step recovery strategy. the proposed approach has been tested successfully to recover a variety of projective transformations between real images.

关键词： Image segmentation Image converters Testing computer vision Image reconstruction Layout Feature extraction Geometry Shape

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View-Invariant Action recognition Using Fundamental Ratios

View-Invariant Action Recognition Using Fundamental Ratios

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Yuping Shen Hassan Foroosh Computational Imaging Laboratory University of Central Florida Orlando FL USA

A moving plane observed by a fixed camera induces a fundamental matrix F across multiple frames, where the ratios among the elements in the upper left 2 × 2 submatrix are herein referred to as the Fundamental Ratios. We show that fundamental ratios are invariant to camera parameters, and hence can be used to identify similar plane motions from varying viewpoints. For action recognition, we decompose a body posture into a set of point triplets (planes). the similarity between two actions is then determined by the motion of point triplets and hence by their associated fundamental ratios, providing thus view-invariant recognition of actions. Results evaluated over 255 semi-synthetic video data with 100 independent trials at a wide range of noise levels, and also on 56 real videos of 8 different classes of actions, confirm that our method can recognize actions under substantial amount of noise, even when they have dynamic timeline maps, and the viewpoints and camera parameters are unknown and totally different.

关键词： Cameras Noise level Image recognition Calibration Humans Anatomy Solid modeling Geometry

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A Bi-Illuminant Dichromatic Reflection Model for Understanding Images

A Bi-Illuminant Dichromatic Reflection Model for Understandi...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Bruce A. Maxwell Richard M. Friedhoff Casey A. Smith Tandent Vision Science Inc.

this paper presents a new model for understanding the appearance of objects that exhibit both body and surface reflection under realistic illumination. Specifically, the model represents the appearance of surfaces that interact with a dominant illuminant and a non-negligible ambient illuminant that may have different spectral power distributions. Real illumination environments usually have an ambient illuminant, and the current dynamic range of consumer cameras is sufficient to capture significant information in shadows. the bi-illuminant dichromatic reflection model explains numerous empirical findings in the literature and has implications for commonly used chromaticity spaces that claim to be illumination invariant but are not in many natural situations. One outcome of the model is the first 2-D chromaticity space for an RGB image that is robust to illumination change given dominant and ambient illuminants with different spectral power distributions.

关键词： Optical reflection Lighting Layout Material properties Colored noise Power distribution Dynamic range Cameras Concrete computer vision

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Context-Dependent Kernel Design for Object Matching and recognition

Context-Dependent Kernel Design for Object Matching and Reco...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Hichem Sahbi Jean-Yves Audibert Jaonary Rabarisoa Renaud Keriven UMR 5141 CNRS Telecom ParisTech France Certis Laboratory ENPC ParisTech France

the success of kernel methods including support vector networks (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to handle fixed-length data, their extension to unordered, variable-length data became more than necessary for real pattern recognition problems such as object recognition and bioinformatics. We focus in this paper on object recognition using a new type of kernel referred to as "context-dependent". Objects, seen as constellations of local features (interest points, regions, etc.), are matched by minimizing an energy function mixing (1) a fidelity term which measures the quality of feature matching, (2) a neighborhood criteria which captures the object geometry and (3) a regularization term. We will show that the fixed-point of this energy is a "context-dependent" kernel ("CDK") which also satisfies the Mercer condition. Experiments conducted on object recognition show that when plugging our kernel in SVMs, we clearly outperform SVMs with "context-free" kernels.

关键词： Kernel Object recognition Support vector machines Face recognition Histograms Telecommunications pattern recognition Bioinformatics Focusing Energy measurement

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Fast Image Search for Learned Metrics

Fast Image Search for Learned Metrics

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Prateek Jain Brian Kulis Kristen Grauman Department of Computer Sciences University of Technology Austin USA

We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the images' underlying relationships well. To allow sub-linear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonly-used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.

关键词： Image databases Spatial databases Extraterrestrial measurements Kernel Indexing Image representation Data structures Digital images computer vision Data mining

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