2011 International Conference on Computer Vision最新文献_第3页

Linear time offline tracking and lower envelope algorithms 线性时间离线跟踪和低包络算法

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126451

Steve Gu, Ying Zheng, Carlo Tomasi

引用次数: 15

Extracting adaptive contextual cues from unlabeled regions 从未标记区域提取适应性上下文线索

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126282

Congcong Li, Devi Parikh, Tsuhan Chen

{"title":"Extracting adaptive contextual cues from unlabeled regions","authors":"Congcong Li, Devi Parikh, Tsuhan Chen","doi":"10.1109/ICCV.2011.6126282","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126282","url":null,"abstract":"Existing approaches to contextual reasoning for enhanced object detection typically utilize other labeled categories in the images to provide contextual information. As a consequence, they inadvertently commit to the granularity of information implicit in the labels. Moreover, large portions of the images may not belong to any of the manually-chosen categories, and these unlabeled regions are typically neglected. In this paper, we overcome both these drawbacks and propose a contextual cue that exploits unlabeled regions in images. Our approach adaptively determines the granularity (scene, inter-object, intra-object, etc.) at which contextual information is captured. In order to extract the proposed contextual cue, we consider a scene to be a structured configuration of objects and regions; just as an object is a composition of parts. We thus learn our proposed “contextual meta-objects” using any off-the-shelf object detector, which makes our proposed cue widely accessible to the community. Our results show that incorporating our proposed cue provides a relative improvement of 12% over a state-of-the-art object detector on the challenging PASCAL dataset.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83168528","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: 47

Multi-view repetitive structure detection 多视图重复结构检测

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126285

Nianjuan Jiang, P. Tan, L. Cheong

引用次数: 23

Panoramic stereo video textures 全景立体视频纹理

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126376

V. Chapdelaine-Couture, M. Langer, S. Roy

引用次数: 28

Sorted Random Projections for robust texture classification 分类随机投影的鲁棒纹理分类

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126267

Li Liu, P. Fieguth, Gangyao Kuang, H. Zha

引用次数: 84

Center-surround divergence of feature statistics for salient object detection 显著目标检测的中心-环绕发散特征统计

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126499

D. A. Klein, S. Frintrop

引用次数: 362

iGroup: Weakly supervised image and video grouping iggroup:弱监督图像和视频分组

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126493

Andrew Gilbert, R. Bowden

{"title":"iGroup: Weakly supervised image and video grouping","authors":"Andrew Gilbert, R. Bowden","doi":"10.1109/ICCV.2011.6126493","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126493","url":null,"abstract":"We present a generic, efficient and iterative algorithm for interactively clustering classes of images and videos. The approach moves away from the use of large hand labelled training datasets, instead allowing the user to find natural groups of similar content based upon a handful of “seed” examples. Two efficient data mining tools originally developed for text analysis; min-Hash and APriori are used and extended to achieve both speed and scalability on large image and video datasets. Inspired by the Bag-of-Words (BoW) architecture, the idea of an image signature is introduced as a simple descriptor on which nearest neighbour classification can be performed. The image signature is then dynamically expanded to identify common features amongst samples of the same class. The iterative approach uses APriori to identify common and distinctive elements of a small set of labelled true and false positive signatures. These elements are then accentuated in the signature to increase similarity between examples and “pull” positive classes together. By repeating this process, the accuracy of similarity increases dramatically despite only a few training examples, only 10% of the labelled groundtruth is needed, compared to other approaches. It is tested on two image datasets including the caltech101 [9] dataset and on three state-of-the-art action recognition datasets. On the YouTube [18] video dataset the accuracy increases from 72% to 97% using only 44 labelled examples from a dataset of over 1200 videos. The approach is both scalable and efficient, with an iteration on the full YouTube dataset taking around 1 minute on a standard desktop machine.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90265761","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: 10

Discriminative learning of relaxed hierarchy for large-scale visual recognition 大规模视觉识别中放松层次的判别学习

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126481

Tianshi Gao, D. Koller

引用次数: 181

Image representation by active curves 用活动曲线表示图像

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126447

Wenze Hu, Y. Wu, Song-Chun Zhu

{"title":"Image representation by active curves","authors":"Wenze Hu, Y. Wu, Song-Chun Zhu","doi":"10.1109/ICCV.2011.6126447","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126447","url":null,"abstract":"This paper proposes a sparse image representation using deformable templates of simple geometric structures that are commonly observed in images of natural scenes. These deformable templates include active curve templates and active corner templates. An active curve template is a composition of Gabor wavelet elements placed with equal spacing on a straight line segment or a circular arc segment of constant curvature, where each Gabor wavelet element is allowed to locally shift its location and orientation, so that the original line and arc segment of the active curve template can be deformed to fit the observed image. An active corner or angle template is a composition of two active curve templates that share a common end point, and the active curve templates are allowed to vary their overall lengths and curvatures, so that the original corner template can deform to match the observed image. This paper then proposes a hierarchical computational architecture of summax maps that pursues a sparse representation of an image by selecting a small number of active curve and corner templates from a dictionary of all such templates. Experiments show that the proposed method is capable of finding sparse representations of natural images. It is also shown that object templates can be learned by selecting and composing active curve and corner templates.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73575825","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: 9

A linear subspace learning approach via sparse coding 一种基于稀疏编码的线性子空间学习方法

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126313

Lei Zhang, Peng Fei Zhu, Q. Hu, D. Zhang

引用次数: 41