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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111-120 订阅

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MKPLS: Manifold Kernel Partial Least Squares for Lipreading and Speaker Identification

MKPLS: Manifold Kernel Partial Least Squares for Lipreading ...

引用

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

作者： Bakry, Amr Elgammal, Ahmed Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9780769549897

Visual speech recognition is a challenging problem, due to confusion between visual speech features. the speaker identification problem is usually coupled with speech recognition. Moreover, speaker identification is important to several applications, such as automatic access control, biometrics, authentication, and personal privacy issues. In this paper, we propose a novel approach for lipreading and speaker identification. We propose a new approach for manifold parameterization in a low-dimensional latent space, where each manifold is represented as a point in that space. We initially parameterize each instance manifold using a nonlinear mapping from a unified manifold representation. We then factorize the parameter space using Kernel Partial Least Squares (KPLS) to achieve a low-dimension manifold latent space. We use two-way projections to achieve two manifold latent spaces, one for the speech content and one for the speaker. We apply our approach on two public databases: AVLetters and OuluVS. We show the results for three different settings of lipreading: speaker independent, speaker dependent, and speaker semi-dependent. Our approach outperforms for the speaker semi-dependent setting by at least 15% of the baseline, and competes in the other two settings.

关键词： audio-visual systems feature extraction least squares approximations speaker recognition

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Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity recognition

Augmenting Bag-of-Words: Data-Driven Discovery of Temporal a...

引用

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

作者： Bettadapura, Vinay Schindler, Grant Ploetz, thomas Essa, Irfan Georgia Inst Technol Atlanta GA 30332 USA Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9780769549897

We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular expressions to discover and encode patterns in activities. We demonstrate the effectiveness of our approach in experimental evaluations where we successfully recognize activities and detect anomalies in four complex datasets.

关键词： image coding image recognition image retrieval topology

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HON4D: Histogram of Oriented 4D Normals for Activity recognition from Depth Sequences

HON4D: Histogram of Oriented 4D Normals for Activity Recogni...

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

作者： Oreifej, Omar Liu, Zicheng Univ Cent Florida Orlando FL 32816 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9780769549897

We present a new descriptor for activity recognition from videos acquired by a depth sensor Previous descriptors mostly compute shape and motion features independently;thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks.

关键词： computational geometry image motion analysis image sensors image sequences normal distribution quantisation (signal)

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Image Understanding from Experts' Eyes by Modeling Perceptual Skill of Diagnostic Reasoning Processes

Image Understanding from Experts' Eyes by Modeling Perceptua...

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

作者： Li, Rui Shi, Pengcheng Haake, Anne R. Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780769549897

Eliciting and representing experts' remarkable perceptual capability of locating, identifying and categorizing objects in images specific to their domains of expertise will benefit image understanding in terms of transferring human domain knowledge and perceptual expertise into image-based computational procedures. In this paper, we present a hierarchical probabilistic framework to summarize the stereotypical and idiosyncratic eye movement patterns shared within 11 board-certified dermatologists while they are examining and diagnosing medical images. Each inferred eye movement pattern characterizes the similar temporal and spatial properties of its corresponding segments of the experts' eye movement sequences. We further discover a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the exhibitions of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies. In each category, images share similar lesion distributions and configurations. the performance of our approach shows that modeling physicians' diagnostic viewing behaviors informs about medical images' understanding to correct diagnosis.

关键词： human factors image classification image sequences medical image processing object recognition probability

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What's in a Name? First Names as Facial Attributes

What's in a Name? First Names as Facial Attributes

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

作者： Chen, Huizhong Gallagher, Andrew C. Girod, Bernd Stanford Univ Stanford CA 94305 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780769549897

this paper introduces a new idea in describing people using their first names, i.e., the name assigned at birth. We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers. We build models for 100 common first names used in the United States and for each pair, construct a pairwise first-name classifier. these classifiers are built using training images downloaded from the internet, with no additional user interaction. this gives our approach important advantages in building practical systems that do not require additional human intervention for labeling. We use the scores from each pairwise name classifier as a set of facial attributes. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. the name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the internet.

关键词： Internet face recognition image classification

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Unsupervised Joint Object Discovery and Segmentation in Internet Images

Unsupervised Joint Object Discovery and Segmentation in Inte...

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

作者： Rubinstein, Michael Joulin, Armand Kopf, Johannes Liu, Ce MIT CSAIL Cambridge MA 02139 USA INRIA Paris France Microsoft Res Beijing Peoples R China

ISBN: (纸本)9780769549897

We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search. the key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present.

关键词： Internet image recognition image retrieval image segmentation object recognition

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Fast Trust Region for Segmentation

Fast Trust Region for Segmentation

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

作者： Gorelick, Lena Schmidt, Frank R. Boykov, Yuri Univ Western Ontario Comp Vis Grp London ON N6A 3K7 Canada Univ Freiburg BIOSS Ctr Biol Signalling Studies Freiburg Germany

ISBN: (纸本)9780769549897

Trust region is a well-known general iterative approach to optimization which offers many advantages over standard gradient descent techniques. In particular, it allows more accurate nonlinear approximation models. In each iteration this approach computes a global optimum of a suitable approximation model within a fixed radius around the current solution, a.k.a. trust region. In general, this approach can be used only when some efficient constrained optimization algorithm is available for the selected nonlinear (more accurate) approximation model. In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms. these energies include, but are not limited to, KL divergence and Bhattacharyya distance between the observed and the target appearance distributions, volume constraint on segment size, and shape prior constraint in a form of.. 2 distance from target shape moments. Our method is 1-2 orders of magnitude faster than the existing state-of-the-art methods while converging to comparable or better solutions.

关键词： computer vision convergence image segmentation iterative methods optimisation

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Multi-Attribute Queries: To Merge or Not to Merge?

Multi-Attribute Queries: To Merge or Not to Merge?

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

作者： Rastegari, Mohammad Diba, Ali Parikh, Devi Farhadi, Ali Univ Maryland College Pk MD 20742 USA Sharif Univ Technol Tehran Iran Virginia Tech Blacksburg VA USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9780769549897

Users often have very specific visual content in mind that they are searching for. the most natural way to communicate this content to an image search engine is to use keywords that specify various properties or attributes of the content. A naive way of dealing with such multi-attribute queries is the following: train a classifier for each attribute independently, and then combine their scores on images to judge their fit to the query. We argue that this may not be the most effective or efficient approach. Conjunctions of attribute often correspond to very characteristic appearances. It would thus be beneficial to train classifiers that detect these conjunctions as a whole. But not all conjunctions result in such tight appearance clusters. So given a multi-attribute query, which conjunctions should we model? An exhaustive evaluation of all possible conjunctions would be time consuming. Hence we propose an optimization approach that identifies beneficial conjunctions.

关键词： content-based retrieval image classification image retrieval object recognition pattern clustering search engines

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Symmetry Detection from Real World Images Competition 2013: Summary and Results

Symmetry Detection from Real World Images Competition 2013: ...

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

作者： Liu, Jingchen Slota, George Zheng, Gang Wu, Zhaohui Park, Minwoo Lee, Seungkyu Rauschert, Ingmar Liu, Yanxi Penn State Univ University Pk PA 16802 USA Object Video San Antonio TX USA Samsung Res Seoul South Korea

ISBN: (纸本)9780769549903

Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. the recent resurging interest in computational symmetry for computer vision and computer graphics applications has motivated us to conduct a US NSF funded symmetry detection algorithm competition as a workshop affiliated with the computer vision and pattern recognition (cvpr) conference, 2013. this competition sets a more complete benchmark for computer vision symmetry detection algorithms. In this report we explain the evaluation metric and the automatic execution of the evaluation workflow. We also present and analyze the algorithms submitted, and show their results on three test sets of real world images depicting reflection, rotation and translation symmetries respectively. this competition establishes a performance baseline for future work on symmetry detection.

关键词： computer graphics computer vision

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Semi-supervised Node Splitting for Random Forest Construction

Semi-supervised Node Splitting for Random Forest Constructio...

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

作者： Liu, Xiao Song, Mingli Tao, Dacheng Liu, Zicheng Zhang, Luming Chen, Chun Bu, Jiajun Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Sydney NSW 2007 Australia Microsoft Res Redmond WA USA

ISBN: (纸本)9780769549897

Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training data. In this paper, we present semi-supervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and unlabeled data. In particular we derive a non-parametric algorithm to obtain an accurate quality measure of splitting by incorporating abundant unlabeled data. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyperplane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. the proposed algorithm is compared with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization and image segmentation. Experimental results on publicly available datasets demonstrate the superiority of our method.

关键词： computer vision learning (artificial intelligence) nonparametric statistics optimisation pattern classification random processes

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