2013 IEEE International Conference on Computer Vision最新文献_第9页

Coherent Object Detection with 3D Geometric Context from a Single Image 基于单幅图像的三维几何背景的相干目标检测

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.320

Jiyan Pan, T. Kanade

引用次数: 12

Robust Tucker Tensor Decomposition for Effective Image Representation 鲁棒塔克张量分解的有效图像表示

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.304

Miao Zhang, C. Ding

引用次数: 16

Visual Reranking through Weakly Supervised Multi-graph Learning 基于弱监督多图学习的视觉重排序

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.323

Cheng Deng, R. Ji, W. Liu, D. Tao, Xinbo Gao

{"title":"Visual Reranking through Weakly Supervised Multi-graph Learning","authors":"Cheng Deng, R. Ji, W. Liu, D. Tao, Xinbo Gao","doi":"10.1109/ICCV.2013.323","DOIUrl":"https://doi.org/10.1109/ICCV.2013.323","url":null,"abstract":"Visual reranking has been widely deployed to refine the quality of conventional content-based image retrieval engines. The current trend lies in employing a crowd of retrieved results stemming from multiple feature modalities to boost the overall performance of visual reranking. However, a major challenge pertaining to current reranking methods is how to take full advantage of the complementary property of distinct feature modalities. Given a query image and one feature modality, a regular visual reranking framework treats the top-ranked images as pseudo positive instances which are inevitably noisy, difficult to reveal this complementary property, and thus lead to inferior ranking performance. This paper proposes a novel image reranking approach by introducing a Co-Regularized Multi-Graph Learning (Co-RMGL) framework, in which the intra-graph and inter-graph constraints are simultaneously imposed to encode affinities in a single graph and consistency across different graphs. Moreover, weakly supervised learning driven by image attributes is performed to denoise the pseudo-labeled instances, thereby highlighting the unique strength of individual feature modality. Meanwhile, such learning can yield a few anchors in graphs that vitally enable the alignment and fusion of multiple graphs. As a result, an edge weight matrix learned from the fused graph automatically gives the ordering to the initially retrieved results. We evaluate our approach on four benchmark image retrieval datasets, demonstrating a significant performance gain over the state-of-the-arts.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"126 1","pages":"2600-2607"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77525433","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: 79

Decomposing Bag of Words Histograms 分解词袋直方图

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.45

Ankit Gandhi, Alahari Karteek, C. V. Jawahar

引用次数: 6

Drosophila Embryo Stage Annotation Using Label Propagation 标签繁殖技术对果蝇胚胎阶段的注释

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.139

T. Kazmar, E. Kvon, A. Stark, Christoph H. Lampert

引用次数: 5

Domain Adaptive Classification 领域自适应分类

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.324

Fatemeh Mirrashed, Mohammad Rastegari

引用次数: 18

Robust Dictionary Learning by Error Source Decomposition 基于错误源分解的鲁棒字典学习

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.276

Zhuoyuan Chen, Ying Wu

{"title":"Robust Dictionary Learning by Error Source Decomposition","authors":"Zhuoyuan Chen, Ying Wu","doi":"10.1109/ICCV.2013.276","DOIUrl":"https://doi.org/10.1109/ICCV.2013.276","url":null,"abstract":"Sparsity models have recently shown great promise in many vision tasks. Using a learned dictionary in sparsity models can in general outperform predefined bases in clean data. In practice, both training and testing data may be corrupted and contain noises and outliers. Although recent studies attempted to cope with corrupted data and achieved encouraging results in testing phase, how to handle corruption in training phase still remains a very difficult problem. In contrast to most existing methods that learn the dictionary from clean data, this paper is targeted at handling corruptions and outliers in training data for dictionary learning. We propose a general method to decompose the reconstructive residual into two components: a non-sparse component for small universal noises and a sparse component for large outliers, respectively. In addition, further analysis reveals the connection between our approach and the ``partial'' dictionary learning approach, updating only part of the prototypes (or informative code words) with remaining (or noisy code words) fixed. Experiments on synthetic data as well as real applications have shown satisfactory performance of this new robust dictionary learning approach.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"122 1","pages":"2216-2223"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86056897","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: 12

Manipulation Pattern Discovery: A Nonparametric Bayesian Approach 操作模式发现:非参数贝叶斯方法

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.172

Bingbing Ni, P. Moulin

{"title":"Manipulation Pattern Discovery: A Nonparametric Bayesian Approach","authors":"Bingbing Ni, P. Moulin","doi":"10.1109/ICCV.2013.172","DOIUrl":"https://doi.org/10.1109/ICCV.2013.172","url":null,"abstract":"We aim to unsupervisedly discover human's action (motion) patterns of manipulating various objects in scenarios such as assisted living. We are motivated by two key observations. First, large variation exists in motion patterns associated with various types of objects being manipulated, thus manually defining motion primitives is infeasible. Second, some motion patterns are shared among different objects being manipulated while others are object specific. We therefore propose a nonparametric Bayesian method that adopts a hierarchical Dirichlet process prior to learn representative manipulation (motion) patterns in an unsupervised manner. Taking easy-to-obtain object detection score maps and dense motion trajectories as inputs, the proposed probabilistic model can discover motion pattern groups associated with different types of objects being manipulated with a shared manipulation pattern dictionary. The size of the learned dictionary is automatically inferred. Comprehensive experiments on two assisted living benchmarks and a cooking motion dataset demonstrate superiority of our learned manipulation pattern dictionary in representing manipulation actions for recognition.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"75 1","pages":"1361-1368"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81230042","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: 2

Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model 基于多模态半监督学习模型的异构图像特征集成

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.218

Xiao Cai, F. Nie, Weidong (Tom) Cai, Heng Huang

{"title":"Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model","authors":"Xiao Cai, F. Nie, Weidong (Tom) Cai, Heng Huang","doi":"10.1109/ICCV.2013.218","DOIUrl":"https://doi.org/10.1109/ICCV.2013.218","url":null,"abstract":"Automatic image categorization has become increasingly important with the development of Internet and the growth in the size of image databases. Although the image categorization can be formulated as a typical multi-class classification problem, two major challenges have been raised by the real-world images. On one hand, though using more labeled training data may improve the prediction performance, obtaining the image labels is a time consuming as well as biased process. On the other hand, more and more visual descriptors have been proposed to describe objects and scenes appearing in images and different features describe different aspects of the visual characteristics. Therefore, how to integrate heterogeneous visual features to do the semi-supervised learning is crucial for categorizing large-scale image data. In this paper, we propose a novel approach to integrate heterogeneous features by performing multi-modal semi-supervised classification on unlabeled as well as unsegmented images. Considering each type of feature as one modality, taking advantage of the large amount of unlabeled data information, our new adaptive multi-modal semi-supervised classification (AMMSS) algorithm learns a commonly shared class indicator matrix and the weights for different modalities (image features) simultaneously.","PeriodicalId":6351,"journal":{"name":"2013 IEEE International Conference on Computer Vision","volume":"5 1","pages":"1737-1744"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85104914","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: 114

Modifying the Memorability of Face Photographs 修改面部照片的可记忆性

2013 IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.397

A. Khosla, Wilma A. Bainbridge, A. Torralba, A. Oliva

引用次数: 107