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Visual concepts for news story tracking: analyzing and exploiting the NIST TRESVID video annotation experiment

Visual concepts for news story tracking: analyzing and explo...

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conference on computer vision and pattern recognition (cvpr)

作者： J.R. Kender M.R. Naphade Computer Science Columbia University New York NY USA Business Informatics IBM Thomas J. Watson Research Center Hawthorne NY USA

In the summer of 2003, using an interactive intelligent tool, over 100 researchers in video understanding annotated from the NIST TRECVID database over 62 hours of news video spanning six months of 1998. These 47K shots with 43 3 K labels from over 1000 visual concept categories comprise the largest publicly available ground truth for this domain. Our analysis of this data, combining the tools of statistical natural language processing, machine learning, and computer vision, finds significant novel statistical patterns that can be exploited for the accurate tracking of the episodes of a given news story over time, by using semantic labels that are solely visual. We find that the ground "truth" is very muddy, but by using the feature selection tool of information gain, we extract 14 reliable visual concepts with mid-frequency use; all but one are visual concepts that refer to settings, rather than actors, objects, or events. We discover that the probability of another episode of a named story to recur after a gap of d days is proportional to 1/(d + 1). We define a novel similarity measure incorporating both semantic and temporal properties between episodes i and j as: Dice(i, j)/(1 + gap(i, j)). We exploit a low-level computer vision technique, normalized cut (Laplacian eigenmaps), for clustering these episodes into stories, and in the process document a weakness of this popular technique. We use these empirical results to make specific recommendations on how better visual semantic ontologies for news stories, and how better video annotation tools, should be designed.

关键词： NIST computer vision Deductive databases Visual databases Data analysis pattern analysis Natural language processing Machine learning Data mining Laplace equations

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Discriminative density propagation for 3D human motion estimation

Discriminative density propagation for 3D human motion estim...

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conference on computer vision and pattern recognition (cvpr)

作者： C. Sminchisescu A. Kanaujia Zhiguo Li D. Metaxas Department of Computer Science University of Toronto Canada Department of Computer Science Rutgers University USA

We describe a mixture density propagation algorithm to estimate 3D human motion in monocular video sequences based on observations encoding the appearance of image silhouettes. Our approach is discriminative rather than generative, therefore it does not require the probabilistic inversion of a predictive observation model. Instead, it uses a large human motion capture data-base and a 3D computer graphics human model in order to synthesize training pairs of typical human configurations together with their realistically rendered 2D silhouettes. These are used to directly learn to predict the conditional state distributions required for 3D body pose tracking and thus avoid using the generative 3D model for inference (the learned discriminative predictors can also be used, complementary, as importance samplers in order to improve mixing or initialize generative inference algorithms). We aim for probabilistically motivated tracking algorithms and for models that can represent complex multivalued mappings common in inverse, uncertain perception inferences. Our paper has three contributions: (1) we establish the density propagation rules for discriminative inference in continuous, temporal chain models; (2) we propose flexible algorithms for learning multimodal state distributions based on compact, conditional Bayesian mixture of experts models; and (3) we demonstrate the algorithms empirically on real and motion capture-based test sequences and compare against nearest-neighbor and regression methods.

关键词： Humans Motion estimation Inference algorithms Biological system modeling Predictive models Video sequences Image coding computer graphics Rendering (computer graphics) Bayesian methods

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Level set evolution without re-initialization: a new variational formulation

Level set evolution without re-initialization: a new variati...

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conference on computer vision and pattern recognition (cvpr)

作者： Chunming Li Chenyang Xu Changfeng Gui M.D. Fox Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA Department of Imaging and Visualiza Siemens Corporate Research Inc. Princeton NJ USA Department of Mathematics University of Connecticut Storrs CT USA

In this paper, we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure. Our variational formulation consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image features, such as object boundaries. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed variational level set formulation has three main advantages over the traditional level set formulations. First, a significantly larger time step can be used for numerically solving the evolution partial differential equation, and therefore speeds up the curve evolution. Second, the level set function can be initialized with general functions that are more efficient to construct and easier to use in practice than the widely used signed distance function. Third, the level set evolution in our formulation can be easily implemented by simple finite difference scheme and is computationally more efficient. The proposed algorithm has been applied to both simulated and real images with promising results.

关键词： Level set Active contours Solid modeling Computed tomography Partial differential equations Image segmentation computer vision Lagrangian functions Mathematics Data visualization

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Cloth representation by shape from shading with shading primitives

Cloth representation by shape from shading with shading prim...

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conference on computer vision and pattern recognition (cvpr)

作者： Feng Han Song-Chun Zhu Department of Computer Science and Statistics University of California Los Angeles Los Angeles CA USA

ISBN: (纸本)0769523722

Cloth is a complex visual pattern with flexible 3D shape and illumination variations. Computing the 3D shape of cloth from a single image is of great interest to both computer graphics and vision researches. However, the acquisition of 3D cloth shape by shape from shading (SFS) is still a challenge. In this paper, we present a two-layer generative model for representing both the 2D cloth image and the 3D cloth surface. The first layer represents all the folds on cloth, which are called "shading primitives" in (Haddon and Forsyth, 1998), and thus captures the overall "skeleton structures" of cloth. We learn a number of typical 3D fold primitives using some training images obtained through photometric stereo. The 3D fold primitives yield a dictionary of 2D shading primitives/or cloth images. The second layer represents non-fold parts with very smooth (often flat) surface or shading, which interpolates the primitives in the first layer with a smoothness prior like conventional SFS. Then we present an algorithm called "cloth sketching" to find all the shading primitives on cloth image and simultaneously recover their 3D shape by fitting to the 3D fold primitives. Our sketch representation can be viewed as a 2-layer Markov random field (MRF), and it introduces some prior knowledge on the folds and has lower dimension and is more robust than the traditional shape-fmm-shading representation which assumes a MRF model on pixels. We show a number of experiments with satisfactory results in comparison to previous work.

关键词： Shape Lighting computer vision computer graphics Skeleton Photometry Dictionaries Surface fitting Markov random fields Robustness

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On the small sample performance of Boosted classifiers

On the small sample performance of Boosted classifiers

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conference on computer vision and pattern recognition (cvpr)

作者： W. Li X. Gao Y. Zhu Visvanathan Ramesh T.E. Boult Computer Science & Engineering Lehigh University Bethlehem PA USA Real-Time Vision & Modeling Siemens Corporate Research Inc. Princeton NJ USA Computer Science University of Colorado Colorado Springs Colorado Springs CO USA

Boosting algorithms have been widely applied in the machine vision systems. Two fundamental issues that have to be solved in these systems are how much training data and how many Boosting rounds are needed to achieve a desired performance. We view the Boosting algorithm as a nonlinear estimation scheme that estimates a strong classifier from a given training sample set (that is generated by sampling a true unknown distribution), the weak classifiers, and the number of Boosting rounds T. The performance characterization of this estimator involves the derivation of the classification error statistics of the trained strong classifier as a function of the training set and the collection of the weak classifiers. Although the convergence and the error bounds for the training error and generalization error of the algorithms have been studied for several years, the estimated bounds are still loose bounds that are only meaningful for large training sets. With no effective tools for determining the error bounds, users are now collecting training samples with as much data as they can afford, with no good way to know if they are sufficient. In this paper, we characterize the classification error statistics of the trained strong classifier as a function of the true distributions of classes, the collection of the weak classifiers, and the size of the training set. We show that the statistics can be numerically computed and the results are more accurate than previous bounds in the literature. Theoretical results are verified through the simulations. Face detection is used as a case study to illustrate the application of the theory on real data.

关键词： Boosting Error analysis Machine vision Training data Character generation Sampling methods Convergence Statistical distributions Computational modeling Face detection

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Efficient mean-shift tracking via a new similarity measure

Efficient mean-shift tracking via a new similarity measure

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conference on computer vision and pattern recognition (cvpr)

作者： Changjiang Yang R. Duraiswami L. Davis Department of Computer Science Perceptual Interfaces and Reality Laboratory University of Maryland College Park MD USA

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.

关键词： Robustness Target tracking Histograms Density measurement Kernel Extraterrestrial measurements Motion measurement Gaussian processes Testing Image sequences

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A two level approach for scene recognition

A two level approach for scene recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Le Lu K. Toyama G.D. Hager Computer Science Department Johns Hopkins University-Applied Physics Laboratory Baltimore MD USA Computer Science Department Johns Hopkins University Baltimore MD One Microsoft Way Microsoft Research Redmond WA

Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-indoor) and multiple category of scene classification. We first learn mixture models for 20 basic classes of local image content based on color and texture information. Once trained, these models are applied to a test image, and produce 20 probability density response maps (PDRM) indicating the likelihood that each image region was produced by each class. We then extract some very simple features from those PDRMs, and use them to train a bagged LDA classifier for 10 scene categories. For this process, no explicit region segmentation or spatial context model are computed. To test this classification system, we created a labeled database of 1500 photos taken under very different environment and lighting conditions, using different cameras, and from 43 persons over 5 years. The classification rate of outdoor-indoor classification is 93.8%, and the classification rate for 10 scene categories is 90.1%. As a byproduct, local image patches can be contextually labeled into the 20 basic material classes by using loopy belief propagation (Yedidia et al., 2001) as an anisotropic filter on PDRMs, producing an image-level segmentation if desired.

关键词： Layout Image segmentation Data mining Linear discriminant analysis Context modeling System testing Image databases Spatial databases Cameras Belief propagation

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Database-guided segmentation of anatomical structures with complex appearance

Database-guided segmentation of anatomical structures with c...

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conference on computer vision and pattern recognition (cvpr)

作者： B. Georgescu X.S. Zhou D. Comaniciu A. Gupta Integrated Data Systems Department Siemens Corporate Research Inc. Princeton NJ USA Computer Aided Diagnosis Siemens Medical Solutions Inc. Malvern PA USA

The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.

关键词： Shape Image segmentation Biomedical imaging Medical diagnostic imaging Spatial databases Ultrasonic imaging Heart Anatomical structure Image databases Data systems

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Robust centerline extraction framework using level sets

Robust centerline extraction framework using level sets

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conference on computer vision and pattern recognition (cvpr)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

In this paper, we present a novel framework for computing centerlines for both 2D and 3D shape analysis. The framework works as follows: an object centerline point is selected automatically as the point of global maximum Euclidean distance from the boundary, and is considered a point source (Ps) that transmits a wave front that evolves over time and traverses the object domain. The front propagates at each object point with a speed that is proportional to its Euclidean distance from the boundary. The motion of the front is governed by a nonlinear partial differential equation whose solution is computed efficiently using level set methods. Initially, the P/sub S/ transmits a moderate speed wave to explore the object domain and extract its topological information such as merging and extreme points. Then, it transmits a new front that is much faster at centerline points than non central ones. As a consequence, centerlines intersect the propagating fronts at those points of maximum positive curvature. Centerlines are computed by tracking them, starting from each topological point until the Ps is reached, by solving an ordinary differential equation using an efficient numerical scheme. The proposed method is computationally inexpensive, handles efficiently objects with complex topology, and computes centerlines that are centered, connected, one point thick, and less sensitive to boundary noise. In addition, the extracted paths form a tree graph without additional cost. We have extensively validated the robustness of the proposed method both quantitatively and qualitatively against several 2D and 3D shapes.

关键词： Robustness Level set Shape Euclidean distance Partial differential equations Data mining Merging Differential equations Topology Noise shaping

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Discriminant Analysis: A Least Squares Approximation View

Discriminant Analysis: A Least Squares Approximation View

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Peng Zhang Jing Peng N. Riedel EECS Department Tulane University New Orleans LA USA Mathematics Department Tulane University New Orleans LA USA

Linear discriminant analysis (LDA) is a very important approach to selecting features in classification such as facial recognition. However it suffers from the small sample size (SSS) problem where LDA cannot be solved numerically. The SSS problem occurs when the number of training samples is less than the number of dimensions, which is often the case in practice. Researchers have proposed several modified versions of LDA to deal with this problem. However, a solid theoretical analysis is missing. In this paper, we analyze LDA and the SSS problem based on learning theory. LDA is derived from Fisher’s criterion. However, when formulated as a least square approximation problem, LDA has a direct connection to regularization network (RN) algorithms. Many learning algorithms such as support vector machines (SVMs) can be viewed as regularization networks. LDA turns out to be an RN without the regularization term, which is in general an ill-posed problem. This explains why LDA suffers from the SSS problem. In order to transform the ill-posed problem into a well-posed one, the regularization term is necessary. Thus, based on statistical learning theory, we derive a new approach to discriminant analysis. We call it discriminant learning analysis (DLA). DLA is wellposed and behaves well in the SSS situation. Experimental results are presented to validate our proposal.

关键词： Least squares approximation Linear discriminant analysis Kernel Statistical learning Mathematics Face recognition Solids Approximation algorithms Machine learning Support vector machines

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