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Learning realistic human actions from movies

Learning realistic human actions from movies

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Ivan Laptev Marcin Marszalek Cordelia Schmid Benjamin Rozenfeld INRIA Rennes IRISA France INRIA Grenoble France LEAR-LJK Bar-llan University Israel

the aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. this challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. the method is shown to improve state-of-the-art results on the standard Kth action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.

关键词： Humans Motion pictures Image recognition Video sharing Layout Text categorization Object recognition Robustness Clothing Cameras

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Characterizing the Shadow Space of Camera-Light Pairs

Characterizing the Shadow Space of Camera-Light Pairs

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Daniel A. Vaquero Rogerio S. Feris Matthew Turk Ramesh Raskar University of California 슠Santa Barbara USA IBM Research USA MIT Media Laboratory USA

We present a theoretical analysis for characterizing the shadows cast by a point light source given its relative position to the camera. In particular, we analyze the epipolar geometry of camera-light pairs, including unusual camera-light configurations such as light sources aligned with the camera's optical axis as well as convenient arrangements such as lights placed in the camera plane. A mathematical characterization of the shadows is derived to determine the orientations and locations of depth discontinuities when projected onto the image plane that could potentially be associated with cast shadows. the resulting theory is applied to compute a lower bound on the number of lights needed to extract all depth discontinuities from a general scene using a multiflash camera. We also provide a characterization of which discontinuities are missed and which are correctly detected by the algorithm, and a foundation for choosing an optimal light placement. Experiments with depth edges computed using two-flash setups and a four-flash setup illustrate the theory, and an additional configuration with a flash at the camera's center of projection is exploited as a solution for some degenerate cases.

关键词： Cameras Image edge detection Light sources Layout Geometrical optics Shape Lighting Optical imaging Image analysis computer vision

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Transfer Learning for Image Classification with Sparse Prototype Representations

Transfer Learning for Image Classification with Sparse Proto...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Ariadna Quattoni Michael Collins Trevor Darrell MIT CSAIL USA University of California Berkeley EECS & ICSI USA

To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function;we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes;we find such a concise representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation. this optimization problem can be formulated as a linear program that can be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particular news topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance.

关键词： Image classification Prototypes Kernel Learning systems Optimization methods computer vision Semisupervised learning Boosting Approximation methods Training data

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Normalized Tree Partitioning for Image Segmentation

Normalized Tree Partitioning for Image Segmentation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Jingdong Wang Yangqing Jia Xian-Sheng Hua Changshui Zhang Long Quan Microsoft Research Asia Beijing China Tsinghua University Beijing China Hong Kong University of Science and Technology Hong Kong China

In this paper, we propose a novel graph based clustering approach with satisfactory clustering performance and low computational cost. It consists of two main steps: tree fitting and partitioning. We first introduce a probabilistic method to fit a tree to a data graph under the sense of minimum entropy. then, we propose a novel tree partitioning method under a normalized cut criterion, called Normalized Tree Partitioning (NTP), in which a fast combinatorial algorithm is designed for exact bipartitioning. Moreover, we extend it to k-way tree partitioning by proposing an efficient best-first recursive bipartitioning scheme. Compared with spectral clustering, NTP produces the exact global optimal bipartition, introduces fewer approximations for k-way partitioning and can intrinsically produce superior performance. Compared with bottom-up aggregation methods, NTP adopts a global criterion and hence performs better. Last, experimental results on image segmentation demonstrate that our approach is more powerful compared with existing graph-based approaches.

关键词： Image segmentation Tree graphs Computational efficiency Joining processes Clustering methods Clustering algorithms Algorithm design and analysis pattern recognition Asia Entropy

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Sketching in the Air: A vision-Based System for 3D Object Design

Sketching in the Air: A Vision-Based System for 3D Object De...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Yu Chen Jianzhuang Liu Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Hong Kong China Microsoft Research Asia Beijing China

3D object design has many applications including flexible 3D sketch input in CAD, computer game, webpage content design, image based object modeling, and 3D object retrieval. Most current 3D object design tools work on a 2D drawing plane such as computer screen or tablet, which is often inflexible with one dimension lost. On the other hand, virtual reality based methods have the drawbacks that there are awkward devices worn by the user and the virtual environment systems are expensive. In this paper, we propose a novel vision-based approach to 3D object design. Our system consists of a PC, a camera, and a mirror. We use the camera and mirror to track a wand so that the user can design 3D objects by sketching in 3D free space directly without having to wear any cumbersome devices. A number of new techniques are developed for working in this system, including input of object wireframes, gestures for editing and drawing objects, and optimization-based planar and curved surface generation. Our system provides designers a new user interface for designing 3D objects conveniently.

关键词： Cameras Mirrors Application software Design automation computer vision Image retrieval Content based retrieval Virtual reality Virtual environment User interfaces

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Shading Models for Illumination and Reflectance Invariant Shape Detectors

Shading Models for Illumination and Reflectance Invariant Sh...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Peter Nillius Josephine Sullivan Antonis Argyros Department of Physics Royal Institute of Technology Stockholm Sweden Computer Science & Communication Royal Institute of Technology Stockholm Sweden Vassilika Vouton Institute of Computer Science-FORTH Heraklion Greece

Many objects have smooth surfaces of a fairly uniform color, thereby exhibiting shading patterns that reveal information about its shape, an important clue to the nature of the object. this papers explores extracting this information from images, by creating shape detectors based on shading. Recent work has derived low-dimensional models of shading that can handle realistic unknown lighting conditions and surface reflectance properties. We extend this theory by also incorporating variations in the surface shape. In doing so it enables the creation of very general models for the 2D appearance of objects, not only coping with variations in illumination and BRDF but also in shape alterations such as small scale and pose changes. Using this framework we propose a scheme to build shading models that can be used for shape detection in a bottom up fashion without any a priori knowledge about the scene. From the developed theory we construct detectors for two basic shape primitives, spheres and cylinders. their performance is evaluated by extensive synthetic experiments as well as experiments on real images.

关键词： Lighting Reflectivity Shape Light sources Frequency Detectors Principal component analysis Data mining Power harmonic filters computer science

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Efficient subdivision-based image and volume warping

Efficient subdivision-based image and volume warping

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Gady Agam Ravinder Singh Department of Computer Science Illinois Institute of Technology Chicago IL USA

Warping is fundamental to multiple algorithms in computer vision and medical imaging such as image and volume registration. Warping is performed by determining a continuous deformation map and applying it to a given image or volume. In registration the deformation map is determined based on correspondence between two images. It is often the case that the deformation map can only be determined at discrete locations and so has to be interpolated. the discrete locations where the deformation map is determined form irregular sampling of the unknown continuous deformation map. thin-plate splines are commonly used to perform the interpolation and provide an optimal solution in the sense of bending energy minimization. Assuming N samples of the deformation map and n{sup}2 image pixels, thin plate splines require solving a N × N dense linear system with O(N{sup}3) complexity for determining spline coefficients and N computations per pixel with O(N n{sup}2) complexity for determining interpolated values. When N and n are large as in the case of volumetric medical image analysis this cost becomes prohibitive. the approach proposed in this paper is based on subdivision surfaces and is capable of achieving similar quality results with O(N log N) complexity for coefficient determination and O(n{sup}2) complexity for computing interpolated values. Experimental results demonstrate two orders of magnitude performance improvement on actual clinical data.

关键词： Interpolation Biomedical imaging Pixel Image registration Deformable models computer vision Image analysis computer science Image sampling Linear systems

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Simultaneous Super-Resolution and Feature Extraction for recognition of Low-Resolution Faces

Simultaneous Super-Resolution and Feature Extraction for Rec...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Pablo H. Hennings-Yeomans Simon Baker B. V. K. Vijaya Kumar ECE Department Carnegie Mellon University USA Microsoft Research Microsoft Corporation USA

Face recognition degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available. Most previous super-resolution approaches are aimed at reconstruction, with recognition only as an after-thought. In contrast, in the proposed method, face features, as they would be extracted for a face recognition algorithm (e.g., eigenfaces, Fisher-faces, etc.), are included in a super-resolution method as prior information. this approach simultaneously provides measures of fit of the super-resolution result, from both reconstruction and recognition perspectives. this is different from the conventional paradigms of matching in a low-resolution domain, or, alternatively, applying a super-resolution algorithm to a low-resolution face and then classifying the super-resolution result. We show, for example, that recognition of faces of as low as 6 × 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low-resolution training set.

关键词： Feature extraction Face recognition Image reconstruction Probes Image resolution Degradation Vectors Data mining Interpolation Surveillance

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A Discriminatively Trained, Multiscale, Deformable Part Model

A Discriminatively Trained, Multiscale, Deformable Part Mode...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Pedro Felzenszwalb David McAllester Deva Ramanan University of Chicago USA Toyota Technological Institute Chicago USA University of California Irvine USA

this paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. the system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

关键词： Deformable models Filters Support vector machines Object detection Data mining Spatial resolution Histograms computer vision Support vector machine classification Costs

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Dimensionality Reduction using Covariance Operator Inverse Regression

Dimensionality Reduction using Covariance Operator Inverse R...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Minyoung Kim Vladimir Pavlovic Department of Computer Science Rutgers University NJ USA

We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical correlation with output targets. DRR is particularly suited for visualization of high dimensional data as well as the efficient regressor design with a reduced input dimension. In this paper we propose a novel nonlinear method for DRR that exploits the kernel Gram matrices of input and output. While most existing DRR techniques rely on the inverse regression, our approach removes the need for explicit slicing of the output space using covariance operators in RKHS. this unique property make DRR applicable to problem domains with high dimensional output data with potentially significant amounts of noise. Although recent kernel dimensionality reduction algorithms make use of RKHS covariance operators to quantify conditional dependency between the input and the targets via the dimension-reduced input, they are either limited to a transduction setting or linear input subspaces and restricted to non-closed-form solutions. In contrast, our approach provides a closed-form solution to the nonlinear basis functions on which any new input point can be easily projected. We demonstrate the benefits of the proposed method in a comprehensive set of evaluations on several important regression problems that arise in computer vision.

关键词： Kernel Linear discriminant analysis Data visualization computer vision Principal component analysis computer science Covariance matrix Closed-form solution Noise reduction Supervised learning

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