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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是41-50 订阅

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Optimizing Over Radial Kernels on Compact Manifolds 27

Optimizing Over Radial Kernels on Compact Manifolds

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Jayasumana, Sadeep Hartley, Richard Salzmann, Mathieu Li, Hongdong Harandi, Mehrtash Australian Natl Univ Canberra ACT Australia NICTA Canberra ACT Australia

ISBN: (纸本)9781479951178

We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recently become increasingly popular in computer vision. However, the number of known positive definite kernels on manifolds remain very limited. Furthermore, most kernels typically depend on at least one parameter that needs to be tuned for the problem at hand. A poor choice of kernel, or of parameter value, may yield significant performance drop-off. Here, we show that positive definite radial kernels on the unit n-sphere, the Grassmann manifold and Kendall's shape manifold can be expressed in a simple form whose parameters can be automatically optimized within a support vector machine framework. We demonstrate the benefits of our kernel learning algorithm on object, face, action and shape recognition.

关键词： Grassmann MKL Riemannian manifolds kernel methods kernels on manifolds shape analysis Manifolds Seeds shape analysis Kernel Positive definite one-parameter kernel method Radial shape recognition

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Incremental Activity Modeling and recognition in Streaming Videos 27

Incremental Activity Modeling and Recognition in Streaming V...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hasan, Mahmudul Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781479951178

Most of the state-of-the-art approaches to human activity recognition in video need an intensive training stage and assume that all of the training examples are labeled and available beforehand. But these assumptions are unrealistic for many applications where we have to deal with streaming videos. In these videos, as new activities are seen, they can be leveraged upon to improve the current activity recognition models. In this work, we develop an incremental activity learning framework that is able to continuously update the activity models and learn new ones as more videos are seen. Our proposed approach leverages upon state-of-the-art machine learning tools, most notably active learning systems. It does not require tedious manual labeling of every incoming example of each activity class. We perform rigorous experiments on challenging human activity datasets, which demonstrate that the incremental activity modeling framework can achieve performance very close to the cases when all examples are available a priori.

关键词： Learning systems

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Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching 27

Learning to Detect Ground Control Points for Improving the A...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Spyropoulos, Aristotle Komodakis, Nikos Mordohai, Philippos Stevens Inst Technol Hoboken NJ 07030 USA Ecole Ponts ParisTech Paris France

ISBN: (纸本)9781479951178

While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel. We show highly competitive results in predicting the correctness of matches and in confidence estimation, which allows us to rank pixels according to the reliability of their assigned disparities. Moreover, we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an MRF-based stereo algorithm. this is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi-dense disparity maps.

关键词： 3D computer vision Stereo correspondence

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Optimal Decisions from Probabilistic Models: the Intersection-over-Union Case 27

Optimal Decisions from Probabilistic Models: the Intersectio...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Nowozin, Sebastian Microsoft Res Cambridge England

ISBN: (纸本)9781479951178

A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models.

关键词： decision theory structured prediction Decision theory Decision problem Probabilistic Model LOSS FUNCTION linear programming images computer vision

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Nonparametric Part Transfer for Fine-grained recognition 27

Nonparametric Part Transfer for Fine-grained Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Goering, Christoph Rodner, Erik Freytag, Alexander Denzler, Joachim Univ Jena Comp Vis Grp D-07745 Jena Germany

ISBN: (纸本)9781479951178

In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. the possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted. Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.

关键词： bird classification fine-grained recognition part detection visual recognition

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three Guidelines of Online Learning for Large-Scale Visual recognition 27

Three Guidelines of Online Learning for Large-Scale Visual R...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ushiku, Yoshitaka Hidaka, Masatoshi Harada, Tatsuya Univ Tokyo Bunkyo Ku Tokyo 113 Japan

ISBN: (纸本)9781479951178

In this paper, we would like to evaluate online learning algorithms for large-scale visual recognition using state-of-the-art features which are preselected and held fixed. Today, combinations of high-dimensional features and linear classifiers are widely used for large-scale visual recognition. Numerous so-called mid-level features have been developed and mutually compared on an experimental basis. Although various learning methods for linear classification have also been proposed in the machine learning and natural language processing literature, they have rarely been evaluated for visual recognition. therefore, we give guidelines via investigations of state-of-the-art online learning methods of linear classifiers. Many methods have been evaluated using toy data and natural language processing problems such as document classification. Consequently, we gave those methods a unified interpretation from the viewpoint of visual recognition. Results of controlled comparisons indicate three guidelines that might change the pipeline for visual recognition.

关键词： Learning algorithms

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Facial Expression recognition via a Boosted Deep Belief Network 27

Facial Expression Recognition via a Boosted Deep Belief Netw...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Ping Han, Shizhong Meng, Zibo Tong, Yan Univ S Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

ISBN: (纸本)9781479951178

A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. this paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.

关键词： Face recognition

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Persistence-based Structural recognition 27

Persistence-based Structural Recognition

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Chunyuan Ovsjanikov, Maks Chazal, Frederic INRIA Saclay Geometr Palaiseau France Ecole Polytech LIX Palaiseau France NIST Gaithersburg MD USA

ISBN: (纸本)9781479951178

this paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, which captures the distribution of values of a given function, persistence diagrams can be used to characterize its structural properties, reflecting spatial information in an invariant way. In practice, the choice of function is simple: each dimension of the feature vector can be viewed as a function. the proposed method is general: it can work on various multimedia data, including 2D shapes, textures and triangle meshes. Extensive experiments on 3D shape retrieval, hand gesture recognition and texture classification demonstrate the performance of the proposed method in comparison with state-of-the-art methods. Additionally, our approach yields higher recognition accuracy when used in conjunction with the bag-of-features.

关键词： Object recognition Topological Data Analysis features persistent homology

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A Hierarchical Context Model for Event recognition in Surveillance Video 27

A Hierarchical Context Model for Event Recognition in Survei...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaoyang Ji, Qiang Rensselaer Polytech Inst Dept ECSE Troy NY 12181 USA

ISBN: (纸本)9781479951178

Due to great challenges such as tremendous intra-class variations and low image resolution, context information has been playing a more and more important role for accurate and robust event recognition in surveillance videos. the context information can generally be divided into the feature level context, the semantic level context, and the prior level context. these three levels of context provide crucial bottom-up, middle level, and top down information that can benefit the recognition task itself. Unlike existing researches that generally integrate the context information at one of the three levels, we propose a hierarchical context model that simultaneously exploits contexts at all three levels and systematically incorporate them into event recognition. To tackle the learning and inference challenges brought in by the model hierarchy, we develop complete learning and inference algorithms for the proposed hierarchical context model based on variational Bayes method. Experiments on VIRAT 1.0 and 2.0 Ground Datasets demonstrate the effectiveness of the proposed hierarchical context model for improving the event recognition performance even under great challenges like large intra-class variations and low image resolution.

关键词： Image resolution

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Submodular Object recognition 27

Submodular Object Recognition

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Fan Jiang, Zhuolin Shao, Ling Univ Sheffield Dept Elect & Elect Engn Sheffield S10 2TN S Yorkshire England Huawei Technol Noahs Ark Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781479951178

We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner. We exploit the benefit of regression for discriminating segments' categories and qualities, where a regressor is trained to each category using the overlapping observations between each figure-ground segment hypothesis and the ground-truth of the target category in an image. Object recognition is achieved by maximizing a submodular objective function, which maximizes the similarities between the selected segments (i.e., facility locations) and their group elements (i.e., clients), penalizes the number of selected segments, and more importantly, encourages the consistency of object categories corresponding to maximum regression values from different category-specific regressors for the selected segments. the proposed framework achieves impressive recognition results on three benchmark datasets, including PASCAL VOC 2007, Caltech-101 and EthZ-shape.

关键词： CPMC Object recognition Regression Submodularity

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