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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是111-120 订阅

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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...

引用

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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Bidirectional Warping of Active Appearance Model

Bidirectional Warping of Active Appearance Model

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

作者： Mollahosseini, Ali Mahoor, Mohammad H. Univ Denver Dept Elect & Comp Engn Denver CO 80210 USA

ISBN: (纸本)9780769549903

Active Appearance Model (AAM) is a commonly used method for facial image analysis with applications in face identification and facial expression recognition. this paper proposes a new approach based on image alignment for AAM fitting called bidirectional warping. Previous approaches warp either the input image or the appearance template. We propose to warp both the input image, using incremental update by an affine transformation, and the appearance template, using an inverse compositional approach. Our experimental results on Multi-PIE face database show that the bidirectional approach outperforms state-of-the-art inverse compositional fitting approaches in extracting landmark points of faces with shape and pose variations.

关键词： affine transforms emotion recognition face recognition feature extraction

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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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Formulating Action recognition as a Ranking Problem

Formulating Action Recognition as a Ranking Problem

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

作者： Can, Ethem F. Manmatha, R. Univ Massachusetts Sch Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9780769549903

Action recognition is one of the major challenges of computer vision. Several approaches have been proposed using different descriptors and multi-class models. In this paper, we focus on binary ranking models for the action recognition problem and address the action recognition as a ranking problem. A binary ranking model is trained for each action and used to recognize the test videos for that action. Binary ranking models are constructed using dense SIFT (DSIFT) descriptors and histogram of oriented gradients / histogram of optical flows (HOG/HOF) descriptors. We show that using ranking models, it is possible to obtain higher recognition accuracies from a baseline that is based on multi-class models on the very recent and challenging benchmark datasets;Human Motion Database (HMDB) and the Action Similarity Labeling (ASLAN).

关键词： computer vision image sequences object recognition video signal processing visual databases

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Scalable Sparse Subspace Clustering

Scalable Sparse Subspace Clustering

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

作者： Peng, Xi Zhang, Lei Yi, Zhang Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Peoples R China

ISBN: (纸本)9780769549897

In this paper, we address two problems in Sparse Sub-space Clustering algorithm (SSC), i.e., scalability issue and out-of-sample problem. SSC constructs a sparse similarity graph for spectral clustering by using l(1)-minimization based coefficients, has achieved state-of-the-art results for image clustering and motion segmentation. However, the time complexity of SSC is proportion to the cubic of problem size such that it is inefficient to apply SSC into large scale setting. Moreover, SSC does not handle with out-of-sample data that are not used to construct the similarity graph. For each new datum, SSC needs recalculating the cluster membership of the whole data set, which makes SSC is not competitive in fast online clustering. To address the problems, this paper proposes out-of-sample extension of SSC, named as Scalable Sparse Subspace Clustering (SSSC), which makes SSC feasible to cluster large scale data sets. the solution of SSSC adopts a "sampling, clustering, coding, and classifying" strategy. Extensive experimental results on several popular data sets demonstrate the effectiveness and efficiency of our method comparing with the state-of-the-art algorithms.

关键词： computational complexity graph theory image classification image coding image motion analysis image sampling image segmentation pattern clustering

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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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A Critical Review of Action recognition Benchmarks

A Critical Review of Action Recognition Benchmarks

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

作者： Hassner, Tal Open Univ Israel Raanana Israel

ISBN: (纸本)9780769549903

Understanding human actions in videos has been a central research theme in computer vision for decades, and much progress has been achieved over the years. Much of this progress was demonstrated on standard benchmarks used to evaluate novel techniques. these benchmarks and their evolution, provide a unique perspective on the growing capabilities of computerized action recognition systems. they demonstrate just how far machine vision systems have come while also underscore the gap that still remains between existing state-of-the-art performance and the needs of real-world applications. In this paper we provide a comprehensive survey of these benchmarks: from early examples, such as the Weizmann set [1], to recently presented, contemporary benchmarks. this paper further provides a summary of the results obtained in the last couple of years on the recent ASLAN benchmark [12], which was designed to reflect the many challenges modern Action recognition systems are expected to overcome.

关键词： computer vision image recognition video signal processing

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