2009 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第2页

Efficient Kernels for identifying unbounded-order spatial features 无界阶空间特征识别的高效核算法

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206791

Yimeng Zhang, Tsuhan Chen

{"title":"Efficient Kernels for identifying unbounded-order spatial features","authors":"Yimeng Zhang, Tsuhan Chen","doi":"10.1109/CVPR.2009.5206791","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206791","url":null,"abstract":"Higher order spatial features, such as doublets or triplets have been used to incorporate spatial information into the bag-of-local-features model. Due to computational limits, researchers have only been using features up to the 3rd order, i.e., triplets, since the number of features increases exponentially with the order. We propose an algorithm for identifying high-order spatial features efficiently. The algorithm directly evaluates the inner product of the feature vectors from two images to be compared, identifying all high-order features automatically. The algorithm hence serves as a kernel for any kernel-based learning algorithms. The algorithm is based on the idea that if a high-order spatial feature co-occurs in both images, the occurrence of the feature in one image would be a translation from the occurrence of the same feature in the other image. This enables us to compute the kernel in time that is linear to the number of local features in an image (same as the bag of local features approach), regardless of the order. Therefore, our algorithm does not limit the upper bound of the order as in previous work. The experiment results on the object categorization task show that high order features can be calculated efficiently and provide significant improvement in object categorization performance.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116060066","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: 49

Learning optimized MAP estimates in continuously-valued MRF models 在连续值MRF模型中学习优化MAP估计

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206774

Kegan G. G. Samuel, M. Tappen

引用次数: 101

Minimizing sparse higher order energy functions of discrete variables 离散变量的稀疏高阶能量函数的最小化

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206739

C. Rother, Pushmeet Kohli, Wei Feng, Jiaya Jia

引用次数: 200

High-quality curvelet-based motion deblurring from an image pair 高质量的基于曲线的运动去模糊图像对

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206711

Jian-Feng Cai, Hui Ji, Chaoqiang Liu, Zuowei Shen

引用次数: 45

Holistic context modeling using semantic co-occurrences 使用语义共现的整体上下文建模

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206826

Nikhil Rasiwasia, N. Vasconcelos

{"title":"Holistic context modeling using semantic co-occurrences","authors":"Nikhil Rasiwasia, N. Vasconcelos","doi":"10.1109/CVPR.2009.5206826","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206826","url":null,"abstract":"We present a simple framework to model contextual relationships between visual concepts. The new framework combines ideas from previous object-centric methods (which model contextual relationships between objects in an image, such as their co-occurrence patterns) and scene-centric methods (which learn a holistic context model from the entire image, known as its “gist”). This is accomplished without demarcating individual concepts or regions in the image. First, using the output of a generic appearance based concept detection system, a semantic space is formulated, where each axis represents a semantic feature. Next, context models are learned for each of the concepts in the semantic space, using mixtures of Dirichlet distributions. Finally, an image is represented as a vector of posterior concept probabilities under these contextual concept models. It is shown that these posterior probabilities are remarkably noise-free, and an effective model of the contextual relationships between semantic concepts in natural images. This is further demonstrated through an experimental evaluation with respect to two vision tasks, viz. scene classification and image annotation, on benchmark datasets. The results show that, besides quite simple to compute, the proposed context models attain superior performance than state of the art systems in both tasks.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519314","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: 49

Large displacement optical flow 大位移光流

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206697

T. Brox, C. Bregler, Jitendra Malik

引用次数: 336

Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates 局部观察，全局推断:用于检测增量更新异常活动的时空磁流变函数

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206569

Jaechul Kim, K. Grauman

{"title":"Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates","authors":"Jaechul Kim, K. Grauman","doi":"10.1109/CVPR.2009.5206569","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206569","url":null,"abstract":"We propose a space-time Markov random field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video frames, and neighboring nodes in both space and time are associated with links. To learn normal patterns of activity at each local node, we capture the distribution of its typical optical flow with a mixture of probabilistic principal component analyzers. For any new optical flow patterns detected in incoming video clips, we use the learned model and MRF graph to compute a maximum a posteriori estimate of the degree of normality at each local node. Further, we show how to incrementally update the current model parameters as new video observations stream in, so that the model can efficiently adapt to visual context changes over a long period of time. Experimental results on surveillance videos show that our space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176137","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: 717

Observable subspaces for 3D human motion recovery 三维人体运动恢复的可观察子空间

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206489

A. Fossati, M. Salzmann, P. Fua

{"title":"Observable subspaces for 3D human motion recovery","authors":"A. Fossati, M. Salzmann, P. Fua","doi":"10.1109/CVPR.2009.5206489","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206489","url":null,"abstract":"The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. However, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. In this paper, we show that by directly using observable quantities as our latent variables, we eliminate this problem and achieve full automation given only modest amounts of training data. More specifically, we exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables. We treat these as our latent variables and learn a mapping between them and sequences of body poses. In this manner, by simply tracking the feet or the hands, we can reliably guess initial poses over whole sequences and, then, refine them.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117261755","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: 19

Modeling images as mixtures of reference images 将图像建模为参考图像的混合物

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206781

F. Perronnin, Yan Liu

{"title":"Modeling images as mixtures of reference images","authors":"F. Perronnin, Yan Liu","doi":"10.1109/CVPR.2009.5206781","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206781","url":null,"abstract":"A state-of-the-art approach to measure the similarity of two images is to model each image by a continuous distribution, generally a Gaussian mixture model (GMM), and to compute a probabilistic similarity between the GMMs. One limitation of traditional measures such as the Kullback-Leibler (KL) divergence and the probability product kernel (PPK) is that they measure a global match of distributions. This paper introduces a novel image representation. We propose to approximate an image, modeled by a GMM, as a convex combination of K reference image GMMs, and then to describe the image as the K-dimensional vector of mixture weights. The computed weights encode a similarity that favors local matches (i.e. matches of individual Gaussians) and is therefore fundamentally different from the KL or PPK. Although the computation of the mixture weights is a convex optimization problem, its direct optimization is difficult. We propose two approximate optimization algorithms: the first one based on traditional sampling methods, the second one based on a variational bound approximation of the true objective function. We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods. We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128384560","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: 4

Automatic registration of LIDAR and optical images of urban scenes 城市场景激光雷达和光学图像的自动配准

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206539

Andrew Mastin, J. Kepner, John W. Fisher III

引用次数: 194