2014 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第4页

Co-localization in Real-World Images 真实世界图像的共定位

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.190

K. Tang, Armand Joulin, Li-Jia Li, Li Fei-Fei

引用次数: 184

Scalable Multitask Representation Learning for Scene Classification 场景分类的可扩展多任务表示学习

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.186

Maksim Lapin, B. Schiele, Matthias Hein

引用次数: 55

StoryGraphs: Visualizing Character Interactions as a Timeline 故事图:将角色互动可视化为时间轴

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.111

Makarand Tapaswi, M. Bäuml, R. Stiefelhagen

引用次数: 62

Predicting Matchability 预测Matchability

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.9

Wilfried Hartmann, M. Havlena, K. Schindler

{"title":"Predicting Matchability","authors":"Wilfried Hartmann, M. Havlena, K. Schindler","doi":"10.1109/CVPR.2014.9","DOIUrl":"https://doi.org/10.1109/CVPR.2014.9","url":null,"abstract":"The initial steps of many computer vision algorithms are interest point extraction and matching. In larger image sets the pairwise matching of interest point descriptors between images is an important bottleneck. For each descriptor in one image the (approximate) nearest neighbor in the other one has to be found and checked against the second-nearest neighbor to ensure the correspondence is unambiguous. Here, we asked the question how to best decimate the list of interest points without losing matches, i.e. we aim to speed up matching by filtering out, in advance, those points which would not survive the matching stage. It turns out that the best filtering criterion is not the response of the interest point detector, which in fact is not surprising: the goal of detection are repeatable and well-localized points, whereas the objective of the selection are points whose descriptors can be matched successfully. We show that one can in fact learn to predict which descriptors are matchable, and thus reduce the number of interest points significantly without losing too many matches. We show that this strategy, as simple as it is, greatly improves the matching success with the same number of points per image. Moreover, we embed the prediction in a state-of-the-art Structure-from-Motion pipeline and demonstrate that it also outperforms other selection methods at system level.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"12 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":"127786627","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: 102

Detecting Objects Using Deformation Dictionaries 使用变形字典检测对象

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.256

Bharath Hariharan, C. L. Zitnick, Piotr Dollár

引用次数: 18

Beyond Human Opinion Scores: Blind Image Quality Assessment Based on Synthetic Scores 超越人类意见分数:基于合成分数的盲图像质量评估

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.540

Peng Ye, J. Kumar, D. Doermann

{"title":"Beyond Human Opinion Scores: Blind Image Quality Assessment Based on Synthetic Scores","authors":"Peng Ye, J. Kumar, D. Doermann","doi":"10.1109/CVPR.2014.540","DOIUrl":"https://doi.org/10.1109/CVPR.2014.540","url":null,"abstract":"State-of-the-art general purpose Blind Image Quality Assessment (BIQA) models rely on examples of distorted images and corresponding human opinion scores to learn a regression function that maps image features to a quality score. These types of models are considered \"opinion-aware\" (OA) BIQA models. A large set of human scored training examples is usually required to train a reliable OA-BIQA model. However, obtaining human opinion scores through subjective testing is often expensive and time-consuming. It is therefore desirable to develop \"opinion-free\" (OF) BIQA models that do not require human opinion scores for training. This paper proposes BLISS (Blind Learning of Image Quality using Synthetic Scores). BLISS is a simple, yet effective method for extending OA-BIQA models to OF-BIQA models. Instead of training on human opinion scores, we propose to train BIQA models on synthetic scores derived from Full-Reference (FR) IQA measures. State-of-the-art FR measures yield high correlation with human opinion scores and can serve as approximations to human opinion scores. Unsupervised rank aggregation is applied to combine different FR measures to generate a synthetic score, which serves as a better \"gold standard\". Extensive experiments on standard IQA datasets show that BLISS significantly outperforms previous OF-BIQA methods and is comparable to state-of-the-art OA-BIQA methods.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"9 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":"125482057","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: 68

Object Partitioning Using Local Convexity 使用局部凸性的对象分区

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.46

S. Stein, Markus Schoeler, Jeremie Papon, F. Wörgötter

{"title":"Object Partitioning Using Local Convexity","authors":"S. Stein, Markus Schoeler, Jeremie Papon, F. Wörgötter","doi":"10.1109/CVPR.2014.46","DOIUrl":"https://doi.org/10.1109/CVPR.2014.46","url":null,"abstract":"The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated data-sets. As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which -- with high accuracy -- represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"15 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":"127961245","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: 163

Face Alignment at 3000 FPS via Regressing Local Binary Features 通过回归局部二进制特征在3000帧/秒的人脸对齐

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.218

Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun

引用次数: 867

Transformation Pursuit for Image Classification 图像分类的变换追求

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.466

Mattis Paulin, Jérôme Revaud, Zaïd Harchaoui, F. Perronnin, C. Schmid

{"title":"Transformation Pursuit for Image Classification","authors":"Mattis Paulin, Jérôme Revaud, Zaïd Harchaoui, F. Perronnin, C. Schmid","doi":"10.1109/CVPR.2014.466","DOIUrl":"https://doi.org/10.1109/CVPR.2014.466","url":null,"abstract":"A simple approach to learning invariances in image classification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a compact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transformations increases training time with no gain in accuracy. We propose a principled algorithm -- Image Transformation Pursuit (ITP) -- for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by selecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an improvement from 70.1% to 74.9% in top-5 accuracy on ImageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"45 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":"129993651","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

The Role of Context for Object Detection and Semantic Segmentation in the Wild 上下文在野外目标检测和语义分割中的作用

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.119

Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, S. Fidler, R. Urtasun, A. Yuille

引用次数: 1203