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Unsupervised learning of categories from sets of partially matching image features

Unsupervised learning of categories from sets of partially m...

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Grauman, Kristen Darrell, Trevor Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139

ISBN: (纸本)0769525970

We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into a space where they cluster according to their partial-match feature correspondences. After efficiently computing the pairwise affinities between the input images in this space, a spectral clustering technique is used to recover the primary groupings among the images. We introduce an efficient means of refining these groupings according to intra-cluster statistics over the subsets of features selected by the partial matches between the images, and based on an optional, variable amount of user supervision. We compute the consistent subsets of feature correspondences within a grouping to infer category feature masks. The output of the algorithm is a partition of the data into a set of learned categories, and a set of classifiers trained from these ranked partitions that can recognize the categories in novel images. © 2006 ieee.

关键词： pattern matching

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Learning boosted asymmetric classifiers for object detection

Learning boosted asymmetric classifiers for object detection

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Hou, Xinwen Liu, Cheng-Lin Tan, Tieniu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science 100080 Beijing China

ISBN: (纸本)0769525970

Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR), and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods. © 2006 ieee.

关键词： Object recognition

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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories

Beyond bags of features: Spatial pyramid matching for recogn...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Lazebnik, Svetlana Schmid, Cordelia Ponce, Jean Beckman Institute University of Illinois INRIA Rhône-Alpes Montbonnot France Ecole Normale Supérieure Paris France

ISBN: (纸本)0769525970

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba's "gist" and Lowe's SIFT descriptors. © 2006 ieee.

关键词： pattern matching

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Robust video-based surveillance by integrating target detection with tracking

Robust video-based surveillance by integrating target detect...

引用

2006 conference on computer vision and pattern recognition workshops

作者： Wang, Junxian Bebis, George Miller, Ronald Computer Vision Laboratory University of Nevada Reno NV Vehicle Design R and A Department Ford Motor Company Dearborn MI

ISBN: (纸本)0769526462

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where detection and tracking are addressed simultaneously in a unified framework (i.e., detection results trigger tracking, and tracking re-enforces detections)to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other (i.e., "detect-then-track"), we feed the results of tracking back to the detection stage to adaptively optimize the threshold used in the detection stage and improve system robustness (i.e., "detect-and-track"). Specifically, the initial locations and representations of the targets are extracted by background subtraction. To model the background, we employ Support Vector Regression (SVR) along with an on-line learning scheme to update it efficiently over time. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and achieve a high shape matching confidence level. Feeding back the results of tracking to the detection stage restricts the range of threshold values, suppress false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing projection distortions. We have validated the proposed framework by detecting vehicles and pedestrians in traffic scenes using both visible and thermal video sequences. Experimental results and comparisons with frame-based detection and kernel-based tracking methods illustrate the robustness of our approach. © 2006 ieee.

关键词： Image processing

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Composite templates for cloth modeling and sketching

Composite templates for cloth modeling and sketching

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Chen, Hong Xu, Zi Jian Liu, Zi Qiang Zhu, Song Chun Departments of Statistics and Computer Science University of California Los Angeles

ISBN: (纸本)0769525970

Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leaf-nodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image. © 2006 ieee.

关键词： Imaging systems

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3D face recognition using 3D alignment for PCA

3D face recognition using 3D alignment for PCA

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Russ, Trina Boehnen, Chris Peters, Tanya Security Technology Dept. Sandia National Labs. Albuquerque NM Computer Science and Engineering University of Notre Dame Notre Dame IN

ISBN: (纸本)0769525970

This paper presents a 3D approach for recognizing faces based on Principal Component Analysis (PCA), The approach addresses the issue of proper 3D face alignment required by PCA for maximum data compression and good generalization performance for new untrained faces. This issue has traditionally been addressed by 2D data normalization, a step that eliminates 3D object size information important for the recognition process. We achieve correspondence of facial points by registering a 3D face to a scaled generic 3D reference face and subsequently perform a surface normal search algorithm. 3D scaling of the generic reference face is performed to enable better alignment of facial points while preserving important 3D size information in the input face. The benefits of this approach for 3D face recognition and dimensionality reduction have been demonstrated on components of the Face recognition Grand Challenge (FRGC) database versions 1 and 2. © 2006 ieee.

关键词： Face recognition

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A projective invariant for textures

A projective invariant for textures

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Yong, Xu Hui, Ji Fermüller, Cornelia Department of Computer Science South China University of Technology Department of Computer Science University of Maryland College Park

ISBN: (纸本)0769525970

Image texture analysis has received a lot of attention in the past years. Researchers have developed many texture signatures based on texture measurements, for the purpose of uniquely characterizing the texture. Existing texture signatures, in general, are not invariant to 3D transforms such as view-point changes and non-rigid deformations of the texture surface, which is a serious limitation for many applications. In this paper, we introduce a new texture signature, called the multifractal spectrum (MFS). It provides an efficient framework combining global spatial invariance and local robust measurements. The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. Experiments demonstrate that the MFS captures the essential structure of textures with quite low dimension. © 2006 ieee.

关键词： Image analysis

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Structure from motion with known camera positions

Structure from motion with known camera positions

引用

2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Carceroni, Rodrigo Kumar, Ankita Daniilidis, Kostas Dept. of Computer and Information Science University of Pennsylvania 3330 Walnut Street Philadelphia PA 19104

ISBN: (纸本)0769525970

The wide availability of GPS sensors is changing the landscape in the applications of structure from motion techniques for localization. In this paper, we study the problem of estimating camera orientations from multiple views, given the positions of the viewpoints in a world coordinate system and a set of point correspondences across the views. Given three or more views, the above problem has a finite number of solutions for three or more point correspondences. Given six or more views, the problem has a finite number of solutions for just two or more points. In the three-view case, we show the necessary and sufficient conditions for the three essential matrices to be consistent with a set of known baselines. We also introduce a method to recover the absolute orientations of three views in world coordinates from their essential matrices. To refine these estimates we perform a least-squares minimization on the group cross product SO(3) × SO(3) × SO(3). We report experiments on synthetic data and on data from the ICCV2005 computer vision Contest. © 2006 ieee.

关键词： Motion estimation

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Stereo matching with symmetric cost functions

Stereo matching with symmetric cost functions

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Yoon, Kuk-Jin Kweon, In So Robotics and Computer Vision Lab. Dept. Electrical Engineering and Computer Science KAIST 373-1 Guseong-dong Yuseong-gu Daejeon 305-701 Korea Republic of

ISBN: (纸本)0769525970

Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global methods mainly focus on how to minimize conventional cost functions efficiently, although it is more important to define cost functions well to improve performance. In this paper, we propose new symmetric cost functions for global stereo methods. We first present a symmetric data cost function for the likelihood and then propose a symmetric discontinuity cost function for the prior in the MRF model for stereo. In defining cost function, both the reference image and the target image are taken into account to improve performance without modeling half-occluded pixels explicitly and without using color segmentation. The performance improvement of stereo matching due to the proposed symmetric cost functions is verified by applying the proposed symmetric cost functions to the belief propagation (BP) based stereo method. Experimental results for standard testbed images show that the performance of the BP based stereo method is greatly improved by the proposed symmetric cost functions. © 2006 ieee.

关键词： pattern matching

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A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image

A dynamic Bayesian network model for autonomous 3d reconstru...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Delage, Erick Lee, Honglak Ng, Andrew Y. Computer Science Department Stanford University Stanford CA 94305

ISBN: (纸本)0769525970

When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floor-wall." geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images. © 2006 ieee.

关键词： Three dimensional computer graphics

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