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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是51-60 订阅

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A Max-Margin Riffled Independence Model for Image Tag Ranking

A Max-Margin Riffled Independence Model for Image Tag Rankin...

引用

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

作者： Lan, Tian Mori, Greg Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780769549897

We propose Max-Margin Riffled Independence Model (MMRIM), a new method for image tag ranking modeling the structured preferences among tags. the goal is to predict a ranked tag list for a given image, where tags are ordered by their importance or relevance to the image content. Our model integrates the max-margin formalism with riffled independence factorizations proposed in [10], which naturally allows for structured learning and efficient ranking. Experimental results on the SUN Attribute and LabelMe datasets demonstrate the superior performance of the proposed model compared with baseline tag ranking methods. We also apply the predicted rank list of tags to several higher-level computer vision applications in image understanding and retrieval, and demonstrate that MMRIM significantly improves the accuracy of these applications.

关键词： computer vision image retrieval

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Learning a Manifold as an Atlas

Learning a Manifold as an Atlas

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

作者： Pitelis, Nikolaos Russell, Chris Agapito, Lourdes Univ London Sch EECS London WC1E 7HU England

ISBN: (纸本)9780769549897

In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining the charts, and the discrete assignment of points to charts. In contrast to existing methods, this direct formulation of a manifold does not require "unwrapping" the manifold into a lower dimensional space and allows us to learn closed manifolds of interest to vision, such as those corresponding to gait cycles or camera pose. We report state-of-the-art results for manifold based nearest neighbour classification on vision datasets, and show how the same techniques can be applied to the 3D reconstruction of human motion from a single image.

关键词： computer vision image motion analysis image reconstruction learning (artificial intelligence) optimisation

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Photometric Ambient Occlusion

Photometric Ambient Occlusion

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

作者： Hauagge, Daniel Wehrwein, Scott Bala, Kavita Snavely, Noah Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

We present a method for computing ambient occlusion (AO) for a stack of images of a scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision, we show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model. We use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark. We also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry.

关键词： computational geometry computer graphics lighting natural scenes

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the SVM-minus Similarity Score for Video Face recognition

The SVM-minus Similarity Score for Video Face Recognition

引用

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

作者： Wolf, Lior Levy, Noga Tel Aviv Univ Blavatnik Sch Comp Sci IL-69978 Tel Aviv Israel

ISBN: (纸本)9780769549897

Face recognition in unconstrained videos requires specialized tools beyond those developed for still images: the fact that the confounding factors change state during the video sequence presents a unique challenge, but also an opportunity to eliminate spurious similarities. Luckily, a major source of confusion in visual similarity of faces is the 3D head orientation, for which image analysis tools provide an accurate estimation. the method we propose belongs to a family of classifier-based similarity scores. We present an effective way to discount pose induced similarities within such a framework, which is based on a newly introduced classifier called SVM-minus. the presented method is shown to outperform existing techniques on the most challenging and realistic publicly available video face recognition benchmark, both by itself, and in concert with other methods.

关键词： face recognition image classification support vector machines video signal processing

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Winding Number for Region-Boundary Consistent Salient Contour Extraction

Winding Number for Region-Boundary Consistent Salient Contou...

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

作者： Ming, Yansheng Li, Hongdong He, Xuming Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9780769549897

this paper aims to extract salient closed contours from an image. For this vision task, both region segmentation cues (e. g. color/texture homogeneity) and boundary detection cues (e. g. local contrast, edge continuity and contour closure) play important and complementary roles. In this paper we show how to combine both cues in a unified framework. the main focus is given to how to maintain the consistency (compatibility) between the region cues and the boundary cues. To this ends, we introduce the use of winding number-a well-known concept in topology-as a powerful mathematical device. By this device, the region-boundary consistency is represented as a set of simple linear relationships. Our method is applied to the figure-ground segmentation problem. the experiments show clearly improved results.

关键词： computer vision feature extraction image segmentation topology

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Occlusion patterns for Object Class Detection

Occlusion Patterns for Object Class Detection

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

作者： Pepik, Bojan Stark, Michael Gehler, Peter Schiele, Bernt Max Planck Inst Informat Saarbrucken Germany Stanford Univ Stanford CA 94305 USA Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (纸本)9780769549897

Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of methods that treat occlusion as just another source of noise instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. these patterns are then used as training data for dedicated detectors of varying sophistication. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid further developments in tackling the occlusion challenge.

关键词： computer graphics hidden feature removal image recognition object detection

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Graph-Based Discriminative Learning for Location recognition

Graph-Based Discriminative Learning for Location Recognition

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

作者： Cao, Song Snavely, Noah Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

Recognizing the location of a query image by matching it to a database is an important problem in computer vision, and one for which the representation of the database is a key issue. We explore new ways for exploiting the structure of a database by representing it as a graph, and show how the rich information embedded in a graph can improve a bag-of-words-based location recognition method. In particular, starting from a graph on a set of images based on visual connectivity, we propose a method for selecting a set of sub-graphs and learning a local distance function for each using discriminative techniques. For a query image, each database image is ranked according to these local distance functions in order to place the image in the right part of the graph. In addition, we propose a probabilistic method for increasing the diversity of these ranked database images, again based on the structure of the image graph. We demonstrate that our methods improve performance over standard bag-of-words methods on several existing location recognition datasets.

关键词： computer vision graph theory image recognition image representation probability query processing visual databases

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Structure Preserving Object Tracking

Structure Preserving Object Tracking

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

作者： Zhang, Lu van der Maaten, Laurens Delft Univ Technol Comp Vis Lab NL-2628 CD Delft Netherlands

ISBN: (纸本)9780769549897

Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (based on tracking-by-detection) that resolves this problem by incorporating spatial constraints between the objects. the spatial constraints are learned along with the object detectors using an online structured SVM algorithm. the experimental evaluation of our structure-preserving object tracker (SPOT) reveals significant performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object.

关键词： computer vision object detection object tracking support vector machines

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Spatiotemporal Deformable Part Models for Action Detection

Spatiotemporal Deformable Part Models for Action Detection

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

作者： Tian, Yicong Sukthankar, Rahul Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9780769549897

Deformable part models have achieved impressive performance for object detection, even on difficult image datasets. this paper explores the generalization of deformable part models from 2D images to 3D spatiotemporal volumes to better study their effectiveness for action detection in video. Actions are treated as spatiotemporal patterns and a deformable part model is generated for each action from a collection of examples. For each action model, the most discriminative 3D subvolumes are automatically selected as parts and the spatiotemporal relations between their locations are learned. By focusing on the most distinctive parts of each action, our models adapt to intra-class variation and show robustness to clutter. Extensive experiments on several video datasets demonstrate the strength of spatiotemporal DPMs for classifying and localizing actions.

关键词： gesture recognition object detection video signal processing

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It's Not Polite To Point: Describing People With Uncertain Attributes

It's Not Polite To Point: Describing People With Uncertain A...

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

作者： Sadovnik, Amir Gallagher, Andrew Chen, Tsuhan Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

Visual attributes are powerful features for many different applications in computer vision such as object detection and scene recognition. Visual attributes present another application that has not been examined as rigorously: verbal communication from a computer to a human. Since many attributes are nameable, the computer is able to communicate these concepts through language. However, this is not a trivial task. Given a set of attributes, selecting a subset to be communicated is task dependent. Moreover, because attribute classifiers are noisy, it is important to find ways to deal with this uncertainty. We address the issue of communication by examining the task of composing an automatic description of a person in a group photo that distinguishes him from the others. We introduce an efficient, principled method for choosing which attributes are included in a short description to maximize the likelihood that a third party will correctly guess to which person the description refers. We compare our algorithm to computer baselines and human describers, and show the strength of our method in creating effective descriptions.

关键词： face recognition feature extraction object detection

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