2011 International Conference on Computer Vision最新文献_第7页

Sparse dictionary-based representation and recognition of action attributes 基于稀疏字典的动作属性表示与识别

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126307

Qiang Qiu, Zhuolin Jiang, R. Chellappa

引用次数: 161

Illumination demultiplexing from a single image 从单个图像进行照明解复用

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126220

Christine Chen, D. Vaquero, M. Turk

引用次数: 3

Linear dependency modeling for feature fusion 特征融合的线性依赖建模

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126477

A. J. Ma, P. Yuen

引用次数: 15

Compact correlation coding for visual object categorization 用于视觉对象分类的紧凑相关编码

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126425

Nobuyuki Morioka, S. Satoh

{"title":"Compact correlation coding for visual object categorization","authors":"Nobuyuki Morioka, S. Satoh","doi":"10.1109/ICCV.2011.6126425","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126425","url":null,"abstract":"Spatial relationships between local features are thought to play a vital role in representing object categories. However, learning a compact set of higher-order spatial features based on visual words, e.g., doublets and triplets, remains a challenging problem as possible combinations of visual words grow exponentially. While the local pairwise codebook achieves a compact codebook of pairs of spatially close local features without feature selection, its formulation is not scale invariant and is only suitable for densely sampled local features. In contrast, the proximity distribution kernel is a scale-invariant and robust representation capturing rich spatial proximity information between local features, but its representation grows quadratically in the number of visual words. Inspired by the two abovementioned techniques, this paper presents the compact correlation coding that combines the strengths of the two. Our method achieves a compact representation that is scaleinvariant and robust against object deformation. In addition, we adopt sparse coding instead of k-means clustering during the codebook construction to increase the discriminative power of our method. We systematically evaluate our method against both the local pairwise codebook and proximity distribution kernel on several challenging object categorization datasets to show performance improvements.","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":"80541833","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: 21

Multiclass transfer learning from unconstrained priors 基于无约束先验的多类迁移学习

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126454

Jie Luo, T. Tommasi, B. Caputo

{"title":"Multiclass transfer learning from unconstrained priors","authors":"Jie Luo, T. Tommasi, B. Caputo","doi":"10.1109/ICCV.2011.6126454","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126454","url":null,"abstract":"The vast majority of transfer learning methods proposed in the visual recognition domain over the last years addresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condition, as it concretely limits the use of this type of approach in several settings: for instance, it does not allow in general to use off-the-shelf models as priors. Moreover, the lack of a multiclass formulation for most of the existing transfer learning algorithms prevents using them for object categorization problems, where their use might be beneficial, especially when the number of categories grows and it becomes harder to get enough annotated data for training standard learning methods. This paper presents a multiclass transfer learning algorithm that allows to take advantage of priors built over different features and with different learning methods than the one used for learning the new task. We use the priors as experts, and transfer their outputs to the new incoming samples as additional information. We cast the learning problem within the Multi Kernel Learning framework. The resulting formulation solves efficiently a joint optimization problem that determines from where and how much to transfer, with a principled multiclass formulation. Extensive experiments illustrate the value of this approach.","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":"89473765","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: 111

Are spatial and global constraints really necessary for segmentation? 空间和全局约束对于分割真的是必要的吗?

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126219

Aurélien Lucchi, Yunpeng Li, X. Boix, Kevin Smith, P. Fua

{"title":"Are spatial and global constraints really necessary for segmentation?","authors":"Aurélien Lucchi, Yunpeng Li, X. Boix, Kevin Smith, P. Fua","doi":"10.1109/ICCV.2011.6126219","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126219","url":null,"abstract":"Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task. In a series of experiments on the PASCAL and the MSRC datasets, we were unable to find evidence of a significant performance increase attributed to the introduction of such constraints. On the contrary, we found that similar levels of performance can be achieved using a much simpler design that essentially ignores these constraints. This more simple approach makes use of the same local and global features to leverage evidence from the image, but instead directly biases the preferences of individual pixels. While our investigation does not prove that spatial and consistency constraints are not useful in principle, it points to the conclusion that they should be validated in a larger context.","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":"89839707","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: 72

Contour Code: Robust and efficient multispectral palmprint encoding for human recognition 轮廓码:用于人类识别的鲁棒和高效的多光谱掌纹编码

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126463

Zohaib Khan, A. Mian, Yiqun Hu

{"title":"Contour Code: Robust and efficient multispectral palmprint encoding for human recognition","authors":"Zohaib Khan, A. Mian, Yiqun Hu","doi":"10.1109/ICCV.2011.6126463","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126463","url":null,"abstract":"We propose ‘Contour Code’, a novel representation and binary hash table encoding for multispectral palmprint recognition. We first present a reliable technique for the extraction of a region of interest (ROI) from palm images acquired with non-contact sensors. The Contour Code representation is then derived from the Nonsubsampled Contourlet Transform. A uniscale pyramidal filter is convolved with the ROI followed by the application of a directional filter bank. The dominant directional subband establishes the orientation at each pixel and the index corresponding to this subband is encoded in the Contour Code representation. Unlike existing representations which extract orientation features directly from the palm images, the Contour Code uses a two stage filtering to extract robust orientation features. The Contour Code is binarized into an efficient hash table structure that only requires indexing and summation operations for simultaneous one-to-many matching with an embedded score level fusion of multiple bands. We quantitatively evaluate the accuracy of the ROI extraction by comparison with a manually produced ground truth. Multispectral palmprint verification results on the PolyU and CASIA databases show that the Contour Code achieves an EER reduction upto 50%, compared to state-of-the-art methods.","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":"90672924","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: 75

Delta-Dual Hierarchical Dirichlet Processes: A pragmatic abnormal behaviour detector Delta-Dual分层狄利克雷过程:一种实用的异常行为检测器

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126497

T. Haines, T. Xiang

引用次数: 24

The NBNN kernel NBNN内核

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126449

T. Tuytelaars, Mario Fritz, Kate Saenko, Trevor Darrell

引用次数: 124

Large-scale image annotation using visual synset 基于视觉同义词集的大规模图像标注

2011 International Conference on Computer Vision Pub Date : 2011-11-06 DOI: 10.1109/ICCV.2011.6126295

David Tsai, Yushi Jing, Yi Liu, H. Rowley, Sergey Ioffe, James M. Rehg

引用次数: 66