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

共 1569 条记录，以下是101-110 订阅

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Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

Rolling Riemannian Manifolds to Solve the Multi-class Classi...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Caseiro, Rui Martins, Pedro Henriques, Joao F. Leite, Fatima Silva Batista, Jorge Univ Coimbra Inst Syst & Robot P-3000 Coimbra Portugal

ISBN: (纸本)9780769549897

In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [,]. A popular framework, valid over any Riemannian manifold, was proposed in [] for binary classification. Once moving from binary to multi-class classification this paradigm is not valid anymore, due to the spread of multiple positive classes on the manifold []. It is then natural to ask whether the multi-class paradigm could be extended to operate on a large class of Riemannian manifolds. We propose a mathematically well-founded classification paradigm that allows to extend the work in [] to multi-class models, taking into account the structure of the space. the idea is to project all the data from the manifold onto an affine tangent space at a particular point. To mitigate the distortion induced by local diffeomorphisms, we introduce for the first time in the computer vision community a well-founded mathematical concept, so-called Rolling map [,] the novelty in this alternate school of thought is that the manifold will be firstly rolled (without slipping or twisting) as a rigid body, then the given data is unwrapped onto the affine tangent space, where the classification is performed.

关键词： computer vision image classification learning (artificial intelligence)

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Optimizing 1-Nearest Prototype Classifiers

Optimizing 1-Nearest Prototype Classifiers

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wohlhart, Paul Koestinger, Martin Donoser, Michael Roth, Peter M. Bischof, Horst Graz Univ Technol Inst Comp Vis & Graph A-8010 Graz Austria

ISBN: (纸本)9780769549897

the development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision. However, the trend towards large scale datasets revived the interest in simpler classifiers to reduce runtime. Simple nearest neighbor classifiers have several beneficial properties, such as low complexity and inherent multi-class handling, however, they have a runtime linear in the size of the database. Recent related work represents data samples by assigning them to a set of prototypes that partition the input feature space and afterwards applies linear classifiers on top of this representation to approximate decision boundaries locally linear. In this paper, we go a step beyond these approaches and purely focus on 1-nearest prototype classification, where we propose a novel algorithm for deriving optimal prototypes in a discriminative manner from the training samples. Our method is implicitly multi-class capable, parameter free, avoids noise overfitting and, since during testing only comparisons to the derived prototypes are required, highly efficient. Experiments demonstrate that we are able to outperform related locally linear methods, while even getting close to the results of more complex classifiers.

关键词： computational complexity computer vision image classification optimisation

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Efficient object detection and segmentation for fine-grained recognition

Efficient object detection and segmentation for fine-grained...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Angelova, Anelia Zhu, Shenghuo NEC Labs Amer Cupertino CA 95014 USA

ISBN: (纸本)9780769549897

We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. the algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also 'zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e. g. birds species. the proposed algorithm is much more efficient than other known methods in similar scenarios [ 4, 21]. Our method is also simpler and we apply it here to different classes of objects, e. g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging large-scale flower dataset, containing 578 species of flowers and 250,000 images.

关键词： image classification image segmentation object detection object recognition

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Generation of Ground Truth for Object Detection While Playing an Online Game: Productive Gaming or Recreational Working?

Generation of Ground Truth for Object Detection While Playin...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kavasidis, Isaak Spampinato, Concetto Giordano, Daniela Univ Catania Catania Italy

ISBN: (纸本)9780769549903

In this paper we present a flash game that aims at generating easily ground truth for testing object detection algorithms. Flash the Fish is an online game where the user is shown videos from underwater environments and has to take photos of fish by clicking on them. the initial ground truth is provided by object detection algorithms and, subsequent, cluster analysis and user evaluation techniques, allow for the generation of ground truth based on the weighted combination of these "photos". Evaluation of the platform and comparison of the obtained results against a hand drawn ground truth confirmed that reliable ground truth generation is not necessarily a cumbersome task both in terms of effort and time needed.

关键词： computer games object detection

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Spatio-Temporal Depth Cuboid Similarity Feature for Activity recognition Using Depth Camera

Spatio-Temporal Depth Cuboid Similarity Feature for Activity...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Xia, Lu Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Dept ECE Austin TX 78712 USA

ISBN: (纸本)9780769549897

Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a filtering method to extract STIPs from depth videos (called DSTIP) that effectively suppress the noisy measurements. Further, we build a novel depth cuboid similarity feature (DCSF) to describe the local 3D depth cuboid around the DSTIPs with an adaptable supporting size. We test this feature on activity recognition application using the public MSRAction3D, MSRDailyActivity3D datasets and our own dataset. Experimental evaluation shows that the proposed approach outperforms state-of-the-art activity recognition algorithms on depth videos, and the framework is more widely applicable than existing approaches. We also give detailed comparisons with other features and analysis of choice of parameters as a guidance for applications.

关键词： cameras feature extraction filtering theory image denoising image recognition video signal processing

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Towards Efficient and Exact MAP-Inference for Large Scale Discrete computer vision Problems via Combinatorial Optimization

Towards Efficient and Exact MAP-Inference for Large Scale Di...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kappes, Joerg Hendrik Speth, Markus Reinelt, Gerhard Schnoerr, Christoph Heidelberg Univ Image & Pattern Anal Grp Bergheimer Str 58 D-69115 Heidelberg Germany Heidelberg Univ Discrete & Combinatorial Optimizat Grp D-69115 Heidelberg Germany

ISBN: (纸本)9780769549897

Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision. While in the last decade research has focused on fast approximative methods, algorithms that provide globally optimal solutions have come more into the research focus in the last years. However, large scale computer vision problems seemed to be out of reach for such methods. In this paper we introduce a promising way to bridge this gap based on partial optimality and structural properties of the underlying problem factorization. Combining these preprocessing steps, we are able to solve grids of size 2048x2048 in less than 90 seconds. On the hitherto unsolvable Chinese character dataset of Nowozin et al. we obtain provably optimal results in 56% of the instances and achieve competitive runtimes on other recent benchmark problems. While in the present work only generalized Potts models are considered, an extension to general graphical models seems to be feasible.

关键词： Markov processes combinatorial mathematics computer vision optimisation

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Perceptual Organization and recognition of Indoor Scenes from RGB-D Images

Perceptual Organization and Recognition of Indoor Scenes fro...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Saurabh Arbelaez, Pablo Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9780769549897

We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gPb - ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies superpixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art.

关键词： feature extraction geometry image classification image coding image colour analysis image resolution image segmentation object detection object recognition

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Hybrid body representation for integrated pose recognition, localization and segmentation

Hybrid body representation for integrated pose recognition, ...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Chen, G. Fan, Guoliang School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States

ISBN: (纸本)9781424422432

We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a "star" graphical model. this hybrid body representation can synergistically integrate pose recognition, localization and segmentation into one computational flow. Moreover, as an important step for feature extraction and model inference, segmentation is involved in the low-level, mid-level and high-level vision stages, where top-down prior knowledge and bottom-up data processing is well integrated via the proposed hybrid body representation. ©2008 ieee.

关键词： Gesture recognition

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Pose from Flow and Flow from Pose

Pose from Flow and Flow from Pose

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Fragkiadaki, Katerina Hu, Han Shi, Jianbo Univ Penn Grasp Lab Philadelphia PA 19104 USA Tsinghua Univ Beijing 100084 Peoples R China

ISBN: (纸本)9780769549897

Human pose detectors, although successful in localising faces and torsos of people, often fail with lower arms. Motion estimation is often inaccurate under fast movements of body parts. We build a segmentation-detection algorithm that mediates the information between body parts recognition, and multi-frame motion grouping to improve both pose detection and tracking. Motion of body parts, though not accurate, is often sufficient to segment them from their backgrounds. Such segmentations are crucial for extracting hard to detect body parts out of their interior body clutter. By matching these segments to exemplars we obtain pose labeled body segments. the pose labeled segments and corresponding articulated joints are used to improve the motion flow fields by proposing kinematically constrained affine displacements on body parts. the pose-based articulated motion model is shown to handle large limb rotations and displacements. Our algorithm can detect people under rare poses, frequently missed by pose detectors, showing the benefits of jointly reasoning about pose, segmentation and motion in videos.

关键词： face recognition image segmentation object detection pose estimation video signal processing

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Cross-View Action recognition via a Continuous Virtual Path

Cross-View Action Recognition via a Continuous Virtual Path

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Zhong Wang, Chunheng Xiao, Baihua Zhou, Wen Liu, Shuang Shi, Cunzhao CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtual views are concatenated into an infinite-dimensional feature to characterize continuous changes from the source to the target view. However, these infinite-dimensional features cannot be used directly. thus, we propose a virtual view kernel to compute the value of similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, there are a lot of unlabeled samples from the target view, which can be utilized to improve the performance of classifiers. thus, we present a constraint strategy to explore the information contained in the unlabeled samples. the rationality behind the constraint is that any action video belongs to only one class. Our method is verified on the IXMAS dataset, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.

关键词： feature extraction image classification image motion analysis video signal processing

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