2014 IEEE Conference on Computer Vision and Pattern Recognition最新文献

Congruency-Based Reranking Congruency-Based Reranking

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.270

Itai Ben-Shalom, Noga Levy, Lior Wolf, N. Dershowitz, Adiel Ben-Shalom, Roni Shweka, Y. Choueka, Tamir Hazan, Yaniv Bar

引用次数: 4

Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric 对偶绝对二次函数分支-剪枝秩和手性约束估计的有效剪枝LMI条件

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.70

A. Habed, D. Paudel, C. Demonceaux, D. Fofi

引用次数: 11

Blind Image Quality Assessment Using Semi-supervised Rectifier Networks 基于半监督整流网络的盲图像质量评估

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.368

Huixuan Tang, Neel Joshi, Ashish Kapoor

引用次数: 106

Unsupervised One-Class Learning for Automatic Outlier Removal 自动离群值去除的无监督单类学习

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.483

W. Liu, G. Hua, John R. Smith

{"title":"Unsupervised One-Class Learning for Automatic Outlier Removal","authors":"W. Liu, G. Hua, John R. Smith","doi":"10.1109/CVPR.2014.483","DOIUrl":"https://doi.org/10.1109/CVPR.2014.483","url":null,"abstract":"Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source. Our approach works under a fully unsupervised manner, differing from traditional one-class learning supervised by known positive labels. By design, our approach optimizes a kernel-based max-margin objective which jointly learns a large margin one-class classifier and a soft label assignment for inliers and outliers. An alternating optimization algorithm is then designed to iteratively refine the classifier and the labeling, achieving a provably convergent solution in only a few iterations. Extensive experiments conducted on four image datasets in the presence of artificial and real-world outliers demonstrate that the proposed approach is considerably superior to the state-of-the-arts in obliterating outliers from contaminated one class of images, exhibiting strong robustness at a high outlier proportion up to 60%.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115597501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 100

Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition 基于simplex的动作识别三维时空特征描述

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.265

Hao Zhang, Wenjun Zhou, Christopher M. Reardon, L. Parker

{"title":"Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition","authors":"Hao Zhang, Wenjun Zhou, Christopher M. Reardon, L. Parker","doi":"10.1109/CVPR.2014.265","DOIUrl":"https://doi.org/10.1109/CVPR.2014.265","url":null,"abstract":"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.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Reconstructing Evolving Tree Structures in Time Lapse Sequences 时间推移序列中进化树结构的重构

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.388

Przemyslaw Glowacki, M. Pinheiro, Engin Türetken, R. Sznitman, Daniel Lebrecht, J. Kybic, A. Holtmaat, P. Fua

引用次数: 10

Three Guidelines of Online Learning for Large-Scale Visual Recognition 大规模视觉识别在线学习的三个准则

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.457

Y. Ushiku, Masatoshi Hidaka, T. Harada

引用次数: 7

Joint Motion Segmentation and Background Estimation in Dynamic Scenes 动态场景中关节运动分割与背景估计

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.54

Adeel Mumtaz, Weichen Zhang, Antoni B. Chan

{"title":"Joint Motion Segmentation and Background Estimation in Dynamic Scenes","authors":"Adeel Mumtaz, Weichen Zhang, Antoni B. Chan","doi":"10.1109/CVPR.2014.54","DOIUrl":"https://doi.org/10.1109/CVPR.2014.54","url":null,"abstract":"We propose a joint foreground-background mixture model (FBM) that simultaneously performs background estimation and motion segmentation in complex dynamic scenes. Our FBM consist of a set of location-specific dynamic texture (DT) components, for modeling local background motion, and set of global DT components, for modeling consistent foreground motion. We derive an EM algorithm for estimating the parameters of the FBM. We also apply spatial constraints to the FBM using an Markov random field grid, and derive a corresponding variational approximation for inference. Unlike existing approaches to background subtraction, our FBM does not require a manually selected threshold or a separate training video. Unlike existing motion segmentation techniques, our FBM can segment foreground motions over complex background with mixed motions, and detect stopped objects. Since most dynamic scene datasets only contain videos with a single foreground object over a simple background, we develop a new challenging dataset with multiple foreground objects over complex dynamic backgrounds. In experiments, we show that jointly modeling the background and foreground segments with FBM yields significant improvements in accuracy on both background estimation and motion segmentation, compared to state-of-the-art methods.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"231 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120890056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Transfer Joint Matching for Unsupervised Domain Adaptation 无监督域自适应的传递联合匹配

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.183

Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S. Yu

引用次数: 620

Posebits for Monocular Human Pose Estimation 用于单目人体姿态估计的波塞比特

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.300

Gerard Pons-Moll, David J. Fleet, B. Rosenhahn

引用次数: 74