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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941-950 订阅

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A Bayesian Approach to Multimodal Visual Dictionary Learning

A Bayesian Approach to Multimodal Visual Dictionary Learning

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Irie, Go Liu, Dong Li, Zhenguo Chang, Shih-Fu NTT Corp Atsugi Kanagawa Japan Columbia Univ New York NY 10027 USA

ISBN: (纸本)9780769549897

Despite significant progress, most existing visual dictionary learning methods rely on image descriptors alone or together with class labels. However, Web images are often associated with text data which may carry substantial information regarding image semantics, and may be exploited for visual dictionary learning. this paper explores this idea by leveraging relational information between image descriptors and textual words via co-clustering, in addition to information of image descriptors. Existing co-clustering methods are not optimal for this problem because they ignore the structure of image descriptors in the continuous space, which is crucial for capturing visual characteristics of images. We propose a novel Bayesian co-clustering model to jointly estimate the underlying distributions of the continuous image descriptors as well as the relationship between such distributions and the textual words through a unified Bayesian inference. Extensive experiments on image categorization and retrieval have validated the substantial value of the proposed joint modeling in improving visual dictionary learning, where our model shows superior performance over several recent methods.

关键词： Internet belief networks image classification image retrieval learning (artificial intelligence) pattern clustering text analysis

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Bottom-up Segmentation for Top-down Detection

Bottom-up Segmentation for Top-down Detection

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Fidler, Sanja Mottaghi, Roozbeh Yuille, Alan Urtasun, Raquel TTI Chicago Chicago IL 60637 USA

ISBN: (纸本)9780769549897

In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. thus our model "blends" between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM [14]. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector [12] on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM [14] in 19 out of 20 classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC' 10 test by 4%.

关键词： computational geometry filtering theory image segmentation object detection pattern clustering

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Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques

Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimens...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zeng, Yun Wang, Chaohui Soatto, Stefano Yau, Shing-Tung Harvard Univ Cambridge MA 02138 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9780769549897

this paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework. Motivated by the observation that many non-local statistics (e.g., shape priors, color distributions) can usually be represented by a small number of parameters, we reformulate the higher-order MRF model by introducing additional latent variables to represent the intrinsic dimensions of the higher-order cliques. the resulting new model, called NC-MRF, not only provides the flexibility in representing the configurations of higher-order cliques, but also automatically decomposes the energy function into less coupled terms, allowing us to design an efficient algorithmic framework for maximum a posteriori (MAP) inference. Based on this novel modeling/inference framework, we achieve state-of-the-art solutions to the challenging problems of class-specific image segmentation and template-based 3D facial expression tracking, which demonstrate the potential of our approach.

关键词： Markov processes face recognition image segmentation inference mechanisms random processes

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Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling

Augmenting CRFs with Boltzmann Machine Shape Priors for Imag...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kae, Andrew Sohn, Kihyuk Lee, Honglak Learned-Miller, Erik Univ Massachusetts Amherst MA 01003 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9780769549897

Conditional random fields (CRFs) provide powerful tools for building models to label image segments. they are particularly well-suited to modeling local interactions among adjacent regions (e.g., superpixels). However, CRFs are limited in dealing with complex, global (long-range) interactions between regions. Complementary to this, restricted Boltzmann machines (RBMs) can be used to model global shapes produced by segmentation models. In this work, we present a new model that uses the combined power of these two network types to build a state-of-the-art labeler. Although the CRF is a good baseline labeler, we show how an RBM can be added to the architecture to provide a global shape bias that complements the local modeling provided by the CRF. We demonstrate its labeling performance for the parts of complex face images from the Labeled Faces in the Wild data set. this hybrid model produces results that are both quantitatively and qualitatively better than the CRF alone. In addition to high-quality labeling results, we demonstrate that the hidden units in the RBM portion of our model can be interpreted as face attributes that have been learned without any attribute-level supervision.

关键词： Boltzmann machines face recognition image segmentation

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Efficient Computation of Shortest Path-Concavity for 3D Meshes

Efficient Computation of Shortest Path-Concavity for 3D Mesh...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zimmer, Henrik Campen, Marcel Kobbelt, Leif Rhein Westfal TH Aachen Comp Graph Grp Aachen Germany

ISBN: (纸本)9780769549897

In the context of shape segmentation and retrieval object-wide distributions of measures are needed to accurately evaluate and compare local regions of shapes. Lien et al. [16] proposed two point-wise concavity measures in the context of Approximate Convex Decompositions of polygons measuring the distance from a point to the polygon's convex hull: an accurate Shortest Path-Concavity (SPC) measure and a Straight Line-Concavity (SLC) approximation of the same. While both are practicable on 2D shapes, the exponential costs of SPC in 3D makes it inhibitively expensive for a generalization to meshes [14]. In this paper we propose an efficient and straight forward approximation of the Shortest Path-Concavity measure to 3D meshes. Our approximation is based on discretizing the space between mesh and convex hull, thereby reducing the continuous Shortest Path search to an efficiently solvable graph problem. Our approach works out-of-the-box on complex mesh topologies and requires no complicated handling of genus. Besides presenting a rigorous evaluation of our method on a variety of input meshes, we also define an SPC-based Shape Descriptor and show its superior retrieval and runtime performance compared with the recently presented results on the Convexity Distribution by Lian et al. [12].

关键词： approximation theory computational geometry graph theory image retrieval image segmentation mesh generation shape recognition

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Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-modality Regression Forest

Unconstrained Monocular 3D Human Pose Estimation by Action D...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yu, Tsz-Ho Kim, Tae-Kyun Cipolla, Roberto Univ Cambridge Cambridge England Imperial Coll London London England

ISBN: (纸本)9780769549897

this work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.

关键词： natural scenes object recognition pose estimation regression analysis spatiotemporal phenomena video signal processing

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Continuous 3D Face Authentication using RGB-D Cameras

Continuous 3D Face Authentication using RGB-D Cameras

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Segundo, Mauricio Pamplona Sarkar, Sudeep Goldgof, Dmitry Silva, Luciano Bellon, Olga Univ S Florida Tampa FL 33620 USA Univ Fed Prana IMAGO Res Grp Parana Brazil

ISBN: (纸本)9780769549903

We present a continuous 3D face authentication system that uses a RGB-D camera to monitor the accessing user and ensure that only the allowed user uses a protected system. At the best of our knowledge, this is the first system that uses 3D face images to accomplish such objective. By using depth images, we reduce the amount of user cooperation that is required by the previous continuous authentication works in the literature. We evaluated our system on four 40 minutes long videos with variations in facial expressions, occlusions and pose, and an equal error rate of 0.8% was achieved.

关键词： authorisation emotion recognition face recognition image colour analysis image sensors pose estimation

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Translation Symmetry Detection: A Repetitive pattern Analysis Approach

Translation Symmetry Detection: A Repetitive Pattern Analysi...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cai, Yunliang Baciu, George Hong Kong Polytech Univ Dept Comp GAMA Lab Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549903

Translation symmetry is one of the most important pattern characteristics in natural and man-made environments. Detecting translation symmetry is a grand challenge in computer vision. this has a large spectrum of real-world applications from industrial settings to design, arts, entertainment and eduction. this paper describes the algorithm we have submitted for the Symmetry Detection Competition 2013. We introduce two new concepts in our symmetric repetitive pattern detection algorithm. the first concept is the bottom-up detection-inference approach. this extends the versatility of current detection methods to a higher level segmentation. the second concept is the framework of a new theoretical analysis of invariant repetitive patterns. this is crucial in symmetry/non-symmetry structure extraction but has less coverage in the previous literature on pattern detection and classification.

关键词： computer vision image segmentation pattern classification

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Exploring Implicit Image Statistics for Visual Representativeness Modeling

Exploring Implicit Image Statistics for Visual Representativ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sun, Xiaoshuai Wang, Xin-Jing Yao, Hongxun Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a computational model of visual representativeness by integrating cognitive theories of representativeness heuristics with computer vision and machine learning techniques. Unlike previous models that build their representativeness measure based on the visible data, our model takes the initial inputs as explicit positive reference and extend the measure by exploring the implicit negatives. Given a group of images that contains obvious visual concepts, we create a customized image ontology consisting of both positive and negative instances by mining the most related and confusable neighbors of the positive concept in ontological semantic knowledge bases. the representativeness of a new item is then determined by its likelihoods for both the positive and negative references. To ensure the effectiveness of probability inference as well as the cognitive plausibility, we discover the potential prototypes and treat them as an intermediate representation of semantic concepts. In the experiment, we evaluate the performance of representativeness models based on both human judgements and user-click logs of commercial image search engine. Experimental results on both ImageNet and image sets of general concepts demonstrate the superior performance of our model against the state-of-the-arts.

关键词： computer vision image representation knowledge based systems learning (artificial intelligence) ontologies (artificial intelligence)

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Fast Energy Minimization using Learned State Filters

Fast Energy Minimization using Learned State Filters

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Guillaumin, Matthieu Van Gool, Luc Ferrari, Vittorio Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9780769549897

Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. these factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. the parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions.

关键词： approximation theory computer vision filtering theory graph theory minimisation

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