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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是71-80 订阅

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Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models 27

Image-based Synthesis and Re-Synthesis of Viewpoints Guided ...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Rematas, Konstantinos Ritschel, Tobias Fritz, Mario Tuytelaars, Tinne Katholieke Univ Leuven IMinds Louvain Belgium Max Planck Inst Informat Munich Germany Univ Saarland D-66123 Saarbrucken Germany

ISBN: (纸本)9781479951178

We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. these novel views can be used to "amplify" training image collections that typically contain only a low number of views or lack certain classes of views entirely (e. g. top views). We extract the correlation of position, normal, reflectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super-resolution and non-local smoothing.

关键词： computer graphics computer vision image-based rendering object detection synthetic training data

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Image Reconstruction from Bag-of-Visual-Words 27

Image Reconstruction from Bag-of-Visual-Words

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kato, Hiroharu Harada, Tatsuya Univ Tokyo Tokyo 1138654 Japan

ISBN: (纸本)9781479951178

the objective of this study is to reconstruct images from Bag-of-Visual-Words (BoVW), which is the de facto standard feature for image retrieval and recognition. BoVW is defined here as a histogram of quantized descriptors extracted densely on a regular grid at a single scale. Despite its wide use, no report describes reconstruction of the original image of a BoVW. this task is challenging for two reasons: 1) BoVW includes quantization errors when local descriptors are assigned to visual words. 2) BoVW lacks spatial information of local descriptors when we count the occurrence of visual words. To tackle this difficult task, we use a large-scale image database to estimate the spatial arrangement of local descriptors. then this task creates a jigsaw puzzle problem with adjacency and global location costs of visual words. Solving this optimization problem is also challenging because it is known as an NP-Hard problem. We propose a heuristic but efficient method to optimize it. To underscore the effectiveness of our method, we apply it to BoVWs extracted from about 100 different categories and demonstrate that it can reconstruct the original images, although the image features lack spatial information and include quantization errors.

关键词： computer vision image reconstruction recognition retrieval

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Dual Linear Regression Based Classification for Face Cluster recognition 27

Dual Linear Regression Based Classification for Face Cluster...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liang Univ No British Columbia Prince George BC V2N 4Z9 Canada

ISBN: (纸本)9781479951178

We are dealing with the face cluster recognition problem where there are multiple images per subject in both gallery and probe sets. It is never guaranteed to have a clear spatio-temporal relation among the multiple images of each subject. Considering that the image vectors of each subject, either in gallery or in probe, span a subspace;an algorithm, Dual Linear Regression Classification (DLRC), for the face cluster recognition problem is developed where the distance between two subspaces is defined as the similarity value between a gallery subject and a probe subject. DLRC attempts to find a "virtual" face image located in the intersection of the subspaces spanning from both clusters of face images. the "distance" between the "virtual" face images reconstructed from both subspaces is then taken as the distance between these two subspaces. We further prove that such distance can be formulated under a single linear regression model where we indeed can find the "distance" without reconstructing the "virtual" face images. Extensive experimental evaluations demonstrated the effectiveness of DLRC algorithm compared to other algorithms.

关键词： Dual Linear Regression Face Cluster recognition Image Set based Face recognition Linear Regression

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Simplex-Based 3D Spatio-Temporal Feature Description for Action recognition 27

Simplex-Based 3D Spatio-Temporal Feature Description for Act...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Hao Zhou, Wenjun Reardon, Christopher Parker, Lynne E. Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781479951178

We present a novel feature description algorithm to describe 3D local spatio-temporal features for human action recognition. Our descriptor avoids the singularity and limited discrimination power issues of traditional 3D descriptors by quantizing and describing visual features in the simplex topological vector space. Specifically, given a feature's support region containing a set of 3D visual cues, we decompose the cues' orientation into three angles, transform the decomposed angles into the simplex space, and describe them in such a space. then, quadrant decomposition is performed to improve discrimination, and a final feature vector is composed from the resulting histograms. We develop intuitive visualization tools for analyzing feature characteristics in the simplex topological vector space. Experimental results demonstrate that our novel simplex-based orientation decomposition (SOD) descriptor substantially outperforms traditional 3D descriptors for the Kth, UCF Sport, and Hollywood-2 benchmark action datasets. In addition, the results show that our SOD descriptor is a superior individual descriptor for action recognition.

关键词： Feature description action recognition simplex spatio-temporal features

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Is Rotation a Nuisance in Shape recognition? 27

Is Rotation a Nuisance in Shape Recognition?

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ke, Quihong Li, Yi Beijing Forestry Univ Beijing Peoples R China NICTA Sydney NSW Australia ANU Sydney NSW Australia

ISBN: (纸本)9781479951178

Rotation in closed contour recognition is a puzzling nuisance in most algorithms. In this paper we address three fundamental issues brought by rotation in shapes: 1) is alignment among shapes necessary? If the answer is "no", 2) how to exploit information in different rotations? and 3) how to use rotation unaware local features for rotation aware shape recognition? We argue that the origin of these issues is the use of hand crafted rotation-unfriendly features and measurements. therefore our goal is to learn a set of hierarchical features that describe all rotated versions of a shape as a class, with the capability of distinguishing different such classes. We propose to rotate shapes as many times as possible as training samples, and learn the hierarchical feature representation by effectively adopting a convolutional neural network. We further show that our method is very efficient because the network responses of all possible shifted versions of the same shape can be computed effectively by re-using information in the overlapping areas. We tested the algorithm on three real datasets: Swedish Leaves dataset, Eth-80 Shape, and a subset of the recently collected Leaf-snap dataset. Our approach used the curvature scale space and outperformed the state of the art.

关键词： Shape recognition convolutional neural network rotation friendly features

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Learning Scalable Discriminative Dictionary with Sample Relatedness 27

Learning Scalable Discriminative Dictionary with Sample Rela...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Feng, Jiashi Jegelka, Stefanie Yan, Shuicheng Darrell, Trevor Natl Univ Singapore Dept ECE Singapore 117548 Singapore Univ Calif Berkeley Dept EECS Berkeley CA USA Univ Calif Berkeley ICSI Berkeley CA USA

ISBN: (纸本)9781479951178

Attributes are widely used as mid-level descriptors of object properties in object recognition and retrieval. Mostly, such attributes are manually pre-defined based on domain knowledge, and their number is fixed. However, pre-defined attributes may fail to adapt to the properties of the data at hand, may not necessarily be discriminative, and/or may not generalize well. In this work, we propose a dictionary learning framework that flexibly adapts to the complexity of the given data set and reliably discovers the inherent discriminative middle-level binary features in the data. We use sample relatedness information to improve the generalization of the learned dictionary. We demonstrate that our framework is applicable to both object recognition and complex image retrieval tasks even with few training examples. Moreover, the learned dictionary also help classify novel object categories. Experimental results on the Animals with Attributes, ILSVRC2010 and PASCAL VOC2007 datasets indicate that using relatedness information leads to significant performance gains over established baselines.

关键词： Object recognition

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Optimizing Average Precision using Weakly Supervised Data 27

Optimizing Average Precision using Weakly Supervised Data

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Behl, Aseem Jawahar, C. V. Kumar, M. Pawan IIIT Hyderabad Hyderabad Andhra Pradesh India Ecole Cent Paris Paris France INRIA Saclay Palaiseau France

ISBN: (纸本)9781479951178

the performance of binary classification tasks, such as action classification and object detection, is often measured in terms of the average precision (AP). Yet it is common practice in computer vision to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. the popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, SVM tends to provide similar accuracy to the AP-SVM classifier, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based binary classifiers on two challenging problems: action classification and character recognition.

关键词： Optimization methods Statistical methods and learning Optimization methods Support Vector Network Character recognition Object detection images computer vision Precision accuracy of measurement Classifiers

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3D Pose from Motion for Cross-view Action recognition via Non-linear Circulant Temporal Encoding 27

3D Pose from Motion for Cross-view Action Recognition via No...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Ankur Martinez, Julieta Little, James J. Woodham, Robert J. Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781479951178

We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data. We achieve this by directly matching purely motion based features from videos to mocap. Our approach recovers 3D pose sequences without performing any body part tracking. We use these matches to generate multiple motion projections and thus add view invariance to our action recognition model. We also introduce a closed form solution for approximate non-linear Circulant Temporal Encoding (nCTE), which allows us to efficiently perform the matches in the frequency domain. We test our approach on the challenging unsupervised modality of the IXMAS dataset, and use publicly available motion capture data for matching. Without any additional annotation effort, we are able to significantly outperform the current state of the art.

关键词： Encoding (symbols)

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Large-scale Video Classification with Convolutional Neural Networks 27

Large-scale Video Classification with Convolutional Neural N...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Karpathy, Andrej Toderici, George Shetty, Sanketh Leung, thomas Sukthankar, Rahul Fei-Fei, Li Google Res Mountain View CA 94043 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9781479951178

Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).

关键词： action classification convolutional dataset large-scale network neural recognition sports video

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Semi-supervised Relational Topic Model for Weakly Annotated Image recognition in Social Media 27

Semi-supervised Relational Topic Model for Weakly Annotated ...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Niu, Zhenxing Hua, Gang Gao, Xinbo Tian, Qi Xidian Univ Xian Peoples R China Stevens Inst Technol Hoboken NJ USA Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9781479951178

In this paper, we address the problem of recognizing images with weakly annotated text tags. Most previous work either cannot be applied to the scenarios where the tags are loosely related to the images;or simply take a pre-fusion at the feature level or a post-fusion at the decision level to combine the visual and textual content. Instead, we first encode the text tags as the relations among the images, and then propose a semi-supervised relational topic model (ss-RTM) to explicitly model the image content and their relations. In such way, we can efficiently leverage the loosely related tags, and build an intermediate level representation for a collection of weakly annotated images. the intermediate level representation can be regarded as a mid-level fusion of the visual and textual content, which is able to explicitly model their intrinsic relationships. Moreover, image category labels are also modeled in the ss-RTM, and recognition can be conducted without training an additional discriminative classifier. Our extensive experiments on social multimedia datasets (images+tags) demonstrated the advantages of the proposed model.

关键词： Image recognition Social Media Tag Topic Model

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