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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是651-660 订阅

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Collective Matrix Factorization Hashing for Multimodal Data 27

Collective Matrix Factorization Hashing for Multimodal Data

引用

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

作者： Ding, Guiguang Guo, Yuchen Zhou, Jile Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)9781479951178

Nearest neighbor search methods based on hashing have attracted considerable attention for effective and efficient large-scale similarity search in computer vision and information retrieval community. In this paper, we study the problems of learning hash functions in the context of multi-modal data for cross-view similarity search. We put forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH). CMFH learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources. We also prove that CMFH, a similarity-preserving hashing learning method, has upper and lower boundaries. Extensive experiments verify that CMFH significantly outperforms several state-of-the-art methods on three different datasets.

关键词： hashing matrix decomposition images computer vision

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Persistence-based Structural recognition 27

Persistence-based Structural Recognition

引用

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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Nonparametric Part Transfer for Fine-grained recognition 27

Nonparametric Part Transfer for Fine-grained Recognition

引用

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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Learning Non-Linear Reconstruction Models for Image Set Classification 27

Learning Non-Linear Reconstruction Models for Image Set Clas...

引用

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

作者： Hayat, Munawar Bennamoun, Mohammed An, Senjian Univ Western Australia Sch Comp Sci & Software Enginnering Nedlands WA 6009 Australia

ISBN: (纸本)9781479951178

We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). the pre-initialized ADNT is then separately trained for images of each class and class-specific models are learnt. Based on the minimum reconstruction error from the learnt class-specific models, a majority voting strategy is used for classification. the proposed framework is extensively evaluated for the task of image set classification based face recognition on Honda/UCSD, CMU Mobo, YouTube Celebrities and a Kinect dataset. Our experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on all these datasets.

关键词： Deep Learning Face recognition Image Set Classification Facial recognition image set images reconstruction error Dataset Image Taxonomy optimum performance

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Saliency Detection on Light Field 27

Saliency Detection on Light Field

引用

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

作者： Li, Nianyi Ye, Jinwei Ji, Yu Ling, Haibin Yu, Jingyi Univ Delaware Newark DE 19716 USA Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781479951178

Existing saliency detection approaches use images as inputs and are sensitive to foreground/background similarities, complex background textures, and occlusions. We explore the problem of using light fields as input for saliency detection. Our technique is enabled by the availability of commercial plenoptic cameras that capture the light field of a scene in a single shot. We show that the unique refocusing capability of light fields provides useful focusness, depths, and objectness cues. We further develop a new saliency detection algorithm tailored for light fields. To validate our approach, we acquire a light field database of a range of indoor and outdoor scenes and generate the ground truth saliency map. Experiments show that our saliency detection scheme can robustly handle challenging scenarios such as similar foreground and background, cluttered background, complex occlusions, etc., and achieve high accuracy and robustness.

关键词： pattern recognition

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Domain Adaptation on the Statistical Manifold 27

Domain Adaptation on the Statistical Manifold

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

作者： Baktashmotlagh, Mahsa Harandi, Mehrtash T. Lovell, Brian C. Salzmann, Mathieu Univ Queensland Brisbane Qld 4072 Australia Australian Natl Univ Canberra ACT 0200 Australia NICTA Canberra ACT Australia

ISBN: (纸本)9781479951178

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions become similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that probability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the corresponding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of-the-art results on a standard object recognition benchmark.

关键词： Domain Adaptation Object recognition Statistical Manifold

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Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform 27

Pyramid-based Visual Tracking Using Sparsity Represented Mea...

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

作者： Zhang, Zhe Wong, Kin Hong Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose a robust method for visual tracking relying on mean shift, sparse coding and spatial pyramids. Firstly, we extend the original mean shift approach to handle orientation space and scale space and name this new method as mean transform. the mean transform method estimates the motion, including the location, orientation and scale, of the interested object window simultaneously and effectively. Secondly, a pixel-wise dense patch sampling technique and a region-wise trivial template designing scheme are introduced which enable our approach to run very accurately and efficiently. In addition, instead of using either holistic representation or local representation only, we apply spatial pyramids by combining these two representations into our approach to deal with partial occlusion problems robustly. Observed from the experimental results, our approach outperforms state-of-theart methods in many benchmark sequences.

关键词： pattern recognition

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Action localization with tubelets from motion 27

Action localization with tubelets from motion

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

作者： Jain, Mihir van Gemert, Jan Jegou, Herve Bouthemy, Patrick Snoek, Cees G. M. Inria Paris France Univ Amsterdam NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9781479951178

this paper considers the problem of action localization, where the objective is to determine when and where certain actions appear. We introduce a sampling strategy to produce 2D+t sequences of bounding boxes, called tubelets. Compared to state-of-the-art alternatives, this drastically reduces the number of hypotheses that are likely to include the action of interest. Our method is inspired by a recent technique introduced in the context of image localization. Beyond considering this technique for the first time for videos, we revisit this strategy for 2D+t sequences obtained from super-voxels. Our sampling strategy advantageously exploits a criterion that reflects how action related motion deviates from background motion. We demonstrate the interest of our approach by extensive experiments on two public datasets: UCF Sports and MSR-II. Our approach significantly outperforms the state-of-the-art on both datasets, while restricting the search of actions to a fraction of possible bounding box sequences.

关键词： pattern recognition

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Efficient Computation of Relative Pose for Multi-Camera Systems 27

Efficient Computation of Relative Pose for Multi-Camera Syst...

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

作者： Kneip, Laurent Li, Hongdong Australian Natl Univ Res Sch Engn Canberra ACT 0200 Australia

ISBN: (纸本)9781479951178

We present a novel solution to compute the relative pose of a generalized camera. Existing solutions are either not general, have too high computational complexity, or require too many correspondences, which impedes an efficient or accurate usage within Ransac schemes. We factorize the problem as a low-dimensional, iterative optimization over relative rotation only, directly derived from well-known epipolar constraints. Common generalized cameras often consist of camera clusters, and give rise to omnidirectional landmark observations. We prove that our iterative scheme performs well in such practically relevant situations, eventually resulting in computational efficiency similar to linear solvers, and accuracy close to bundle adjustment, while using less correspondences. Experiments on both virtual and real multi-camera systems prove superior overall performance for robust, real-time multi-camera motion-estimation.

关键词： computer vision Generalized Camera Relative Pose Structure from Motion

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Multimodal Learning in Loosely-organized Web Images 27

Multimodal Learning in Loosely-organized Web Images

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

作者： Duan, Kun Crandall, David J. Batra, Dhruv Indiana Univ Bloomington IN 47405 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781479951178

Photo-sharing websites have become very popular in the last few years, leading to huge collections of online images. In addition to image data, these websites collect a variety of multimodal metadata about photos including text tags, captions, GPS coordinates, camera metadata, user profiles, etc. However, this metadata is not well constrained and is often noisy, sparse, or missing altogether. In this paper, we propose a framework to model these "loosely organized" multimodal datasets, and show how to perform loosely-supervised learning using a novel latent Conditional Random Field framework. We learn parameters of the LCRF automatically from a small set of validation data, using Information theoretic Metric Learning (ITML) to learn distance functions and a structural SVM formulation to learn the potential functions. We apply our framework on four datasets of images from Flickr, evaluating both qualitatively and quantitatively against several baselines.

关键词： graphical models multimodal image modeling object recognition semi-supervised learning

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