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Cost-Sensitive Face recognition

Cost-Sensitive Face Recognition

引用

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

作者： Yin Zhang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Traditional face recognition systems attempt to achieve a high recognition accuracy, which implicitly assumes that the losses of all misclassifications are the same. However, in many real-world tasks this assumption is not always reasonable. For example, it will be troublesome if a face-recognition-based door-locker misclassifies a family member as a stranger such that s/he were not allowed to enter the house;but it will be a much more serious disaster if a stranger were misclassified as a family member and allowed to enter the house. In this paper, we propose a framework which formulates the problem as a multi-class cost-sensitive learning task, and propose a theoretically sound method based on Bayes decision theory to solve this problem. Experimental results demonstrate the effectiveness and efficiency of the proposed method.

关键词： Face recognition Costs Machine learning Laboratories Decision theory Image recognition Data mining Classification algorithms Machine learning algorithms Loss measurement

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pattern Discovery in Motion Time Series via Structure-based Spectral Clustering

Pattern Discovery in Motion Time Series via Structure-based ...

引用

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

作者： Xiaozhe Wang Liang Wang Anthony Wirth Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

this paper proposes an approach called 'structure-based spectral clustering' to identify clusters in motion time series for sequential pattern discovery. the proposed approach deploys a 'statistical feature-based distance computation' for spectral clustering algorithm. Compared to traditional spectral clustering approaches, in which the similarity matrix is constructed from the original data points by applying some similarity functions, the proposed approach builds the matrix based on a finite set of feature vectors. When the proposed approach uses less data points and simpler similarity function to computing the similarity matrix input for spectral clustering, it can improve the computational efficiency in constructing the similarity graph in spectral clustering compared to conventional approach. Promising experimental results with high accuracy on real world data sets demonstrate the capability and effectiveness of the proposed approach for pattern discovery in motion video sequences.

关键词： Clustering algorithms Data mining Computational efficiency Time measurement Feature extraction Hidden Markov models Video sequences pattern recognition computer vision Indexing

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Motion Estimation for Multi-Camera Systems using Global Optimization

Motion Estimation for Multi-Camera Systems using Global Opti...

引用

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

作者： Jae-Hak Kim Hongdong Li Richard Hartley NICTA Australian National University Australia

We present a motion estimation algorithm for multi-camera systems consisting of more than one calibrated camera securely attached on a moving object. So, they move all together, but do not require to have overlapping views across the cameras. the geometrically optimal solution of the motion for the multi-camera systems under L{sub}∞ norm is provided in this paper using a global optimization technique which has been introduced recently in the computer vision research field. Taking advantage of an optimal estimate of the essential matrix through searching rotation space, we provide the optimal solution for translation by using linear programming and Branch & Bound algorithm. Synthetic and real data experiments are conducted, and they show more robust and improved performance than the previous methods.

关键词： Motion estimation Cameras Linear programming Vehicles computer vision Robustness Large-scale systems Broadband communication Australia Council Image reconstruction

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Image Partial Blur Detection and Classification

Image Partial Blur Detection and Classification

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Renting Liu Zhaorong Li Jiaya Jia Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information.

关键词： Data mining Image analysis Kernel Image restoration Motion analysis Motion detection Image segmentation Information analysis Image color analysis computer science

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Integrated Feature Selection and Higher-order Spatial Feature Extraction for Object Categorization

Integrated Feature Selection and Higher-order Spatial Featur...

引用

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

作者： David Liu Gang Hua Paul Viola Tsuhan Chen Department of ECE Carnegie Mellon University USA Department of ECE Microsoft Live Laboratories Research USA

In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature selection and feature extraction. Higher-order spatial features are progressively extracted based on selected lower order ones, thereby avoiding exhaustive computation. the method can be based on any additive feature selection algorithm such as boosting. Experimental results show that the method is computationally much more efficient than previous approaches, without sacrificing accuracy.

关键词： Feature extraction Boosting Image representation Pipelines Pixel computer vision pattern recognition Random access memory Object recognition Testing

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Learning the Viewpoint Manifold for Action recognition

Learning the Viewpoint Manifold for Action Recognition

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Richard Souvenir Justin Babbs Department of Computer Science North Carolina State University Charlotte USA

Researchers are increasingly interested in providing video-based, view-invariant action recognition for human motion. Addressing this problem will lead to more accurate modeling and analysis of the type of unconstrained video commonly collected in the areas of athletics and medicine. Previous viewpoint-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available data set previously only acheieved using multiple simultaneous views.

关键词： Cameras Humans Testing Motion analysis Robustness computer science Biomedical imaging Image motion analysis computer vision Application software

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On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery

On Handling Uncertainty in the Fundamental Matrix for Scene ...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Sreenivas R. Sukumar Hamparsum Bozdogan David L. Page Andreas F. Koschan Mongi A. Abidi Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA

the estimation of the fundamental matrix is the key step in feature-based camera ego-motion estimation for applications in scene modeling and vehicle navigation. In this paper, we present a new method of analyzing and further reducing the risk in the fundamental matrix due to the choice of a particular feature detector, the choice of the matching algorithm, the motion model, iterative hypothesis generation and verification paradigms. Our scheme makes use of model-selection theory to guide the switch to optimal methods for fundamental matrix estimation within the hypothesis-and-test architecture. We demonstrate our proposed method for vision-based robot localization in large-scale environments where the environment is constantly changing and navigation within the environment is unpredictable.

关键词： Uncertainty Layout Navigation Switches Cameras Vehicles Risk analysis Algorithm design and analysis computer vision Motion detection

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Optimised KD-trees for fast image descriptor matching

Optimised KD-trees for fast image descriptor matching

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Chanop Silpa-Anan Richard Hartley Seeing Machines Canberra Australia NICTA Australian National University Australia

In this paper, we look at improving the KD-tree for a specific usage: indexing a large number of SIFT and other types of image descriptors. We have extended priority search, to priority search among multiple trees. By creating multiple KD-trees from the same data set and simultaneously searching among these trees, we have improved the KD-tree's search performance significantly. We have also exploited the structure in SIFT descriptors (or structure in any data set) to reduce the time spent in backtracking. By using Principal Component Analysis to align the principal axes of the data with the coordinate axes, we have further increased the KD-tree's search performance.

关键词： Application software Image recognition Image databases Indexing Principal component analysis Binary search trees Binary trees computer vision Image retrieval Search methods

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3D Occlusion Recovery using Few Cameras

3D Occlusion Recovery using Few Cameras

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Mark Keck James W. Davis Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present a practical framework for detecting and modeling 3D static occlusions for wide-baseline, multi-camera scenarios where the number of cameras is small. the framework consists of an iterative learning procedure where at each frame the occlusion model is used to solve the voxel occupancy problem, and this solution is then used to update the occlusion model. Along with this iterative procedure, there are two contributions of the proposed work: (1) a novel energy function (which can be minimized via graph cuts) specifically designed for use in this procedure, and (2) an application that incorporates our probabilistic occlusion model into a 3D tracking system. Both qualitative and quantitative results of the proposed algorithm and its incorporation with a 3D tracker are presented for support.

关键词： Cameras Image reconstruction Layout Iterative algorithms Surveillance computer vision computer science Application software Robustness Feature extraction

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Robust Unambiguous Parametrization of the Essential Manifold

Robust Unambiguous Parametrization of the Essential Manifold

引用

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

作者： Raghav Subbarao Yakup Genc Peter Meer Real-time Vision and Modelling Department Siemens AG Corporate Research and Development Princeton NJ USA ECE Department Rutgers University Piscataway NJ USA

Analytic manifolds were recently used for motion averaging, segmentation and robust estimation. Here we consider the epipolar constraint for calibrated cameras, which is the most general motion model for calibrated cameras and is encoded by the essential matrix. the set of all essential matrices forms the essential manifold. We provide a theoretical characterization of the geometry of the essential manifold and develop a parametrization which associates each essential matrix with a unique point on the manifold. Our work provides a more complete theoretical analysis of the essential manifold than previous work in this direction. We show the results of using this parametrization with real data sets, while previous work concentrated on theoretical analysis with synthetic data.

关键词： Robustness Cameras Geometry computer vision Motion analysis Motion estimation Motion segmentation Data analysis Clustering algorithms Layout

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