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

共 654 条记录，以下是1-10 订阅

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26th ieee conference on computer vision and pattern recognition, cvpr

26th IEEE Conference on Computer Vision and Pattern Recognit...

引用

26th ieee conference on computer vision and pattern recognition, cvpr

ISBN: (纸本)9781424422432

the proceedings contain 506 papers. the topics discussed include: face shape recovery from a single image using CCA mapping between tensor spaces;classifiability-based optimal discriminatory projection pursuit;blindly separating mixtures of multiple layers with spatial shifts;structure-perceptron learning of a hierarchical log-linear model;unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition;classification via semi-Riemannian spaces;mining compositional features for boosting;context-aware clustering;locally adaptive learning for translation-variant MRF image priors;semi-supervised SVM batch mode active learning for image retrieval;semi-supervised distance metric learning for collaborative image retrieval;multiple-instance ranking: learning to rank images for image retrieval;correlational spectral clustering;and a parallel decomposition solver for SVM: distributed dual ascend using fenchel duality.

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pattern-Driven Colorization of 3D Surfaces

Pattern-Driven Colorization of 3D Surfaces

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

作者： Leifman, George Tal, Ayellet Technion Israel Inst Technol IL-32000 Haifa Israel

ISBN: (纸本)9780769549897

Colorization refers to the process of adding color to black & white images or videos. this paper extends the term to handle surfaces in three dimensions. this is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focus on surfaces with patterns and propose a novel algorithm for adding colors to these surfaces. the user needs only to scribble a few color strokes on one instance of each pattern, and the system proceeds to automatically colorize the whole surface. For this scheme to work, we address not only the problem of colorization, but also the problem of pattern detection on surfaces.

关键词： computer graphics image colour analysis image recognition image texture

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Sparse Output Coding for Large-Scale Visual recognition

Sparse Output Coding for Large-Scale Visual Recognition

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

作者： Zhao, Bin Xing, Eric P. Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach.

关键词： decoding image classification learning (artificial intelligence) matrix algebra object recognition probability

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Online Robust Dictionary Learning

Online Robust Dictionary Learning

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

作者： Lu, Cewu Shi, Jianping Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision. It, however faces the major difficulty to incorporate robust functions, rather than the square data fitting term, to handle outliers in training data. In this paper we propose a new online framework enabling the use of l(1) sparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique. Extensive experiments have been carried out to validate our new framework.

关键词： computer vision data handling dictionaries learning (artificial intelligence)

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Continuous Inference in Graphical Models with Polynomial Energies

Continuous Inference in Graphical Models with Polynomial Ene...

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

作者： Salzmann, Mathieu NICTA Canberra ACT Australia

ISBN: (纸本)9780769549897

In this paper, we tackle the problem of performing inference in graphical models whose energy is a polynomial function of continuous variables. Our energy minimization method follows a dual decomposition approach, where the global problem is split into subproblems defined over the graph cliques. the optimal solution to these subproblems is obtained by making use of a polynomial system solver. Our algorithm inherits the convergence guarantees of dual decomposition. To speed up optimization, we also introduce a variant of this algorithm based on the augmented Lagrangian method. Our experiments illustrate the diversity of computer vision problems that can be expressed with polynomial energies, and demonstrate the benefits of our approach over existing continuous inference methods.

关键词： computer vision graph theory minimisation polynomials

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Semi-supervised Learning with Constraints for Person Identification in Multimedia Data

Semi-supervised Learning with Constraints for Person Identif...

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

作者： Baeuml, Martin Tapaswi, Makarand Stiefelhagen, Rainer Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9780769549897

We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. the method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.

关键词： face recognition learning (artificial intelligence) multimedia databases regression analysis

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Adaptive Active Learning for Image Classification

Adaptive Active Learning for Image Classification

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

作者： Li, Xin Guo, Yuhong Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9780769549897

Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computer vision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computer vision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.

关键词： computer vision image classification learning (artificial intelligence) natural scenes object recognition query processing

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Probabilistic Label Trees for Efficient Large Scale Image Classification

Probabilistic Label Trees for Efficient Large Scale Image Cl...

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

作者： Liu, Baoyuan Sadeghi, Fereshteh Tappen, Marshall Shamir, Ohad Liu, Ce Univ Cent Florida Orlando FL 32816 USA Microsoft Res Boston NE USA

ISBN: (纸本)9780769549897

Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. the label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In this paper we show how the parameters of the label tree can be found using maximum likelihood estimation. this new probabilistic learning technique produces a label tree with significantly improved recognition accuracy.

关键词： image classification image recognition maximum likelihood estimation probability trees (mathematics)

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Learning Cross-domain Information Transfer for Location recognition and Clustering

Learning Cross-domain Information Transfer for Location Reco...

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

作者： Gopalan, Raghuraman AT & T Labs Res Video & Multimedia Technol Res Dept Middletown NJ 07748 USA

ISBN: (纸本)9780769549897

Estimating geographic location from images is a challenging problem that is receiving recent attention. In contrast to many existing methods that primarily model discriminative information corresponding to different locations, we propose joint learning of information that images across locations share and vary upon. Starting with generative and discriminative subspaces pertaining to domains, which are obtained by a hierarchical grouping of images from adjacent locations, we present a top-down approach that first models cross-domain information transfer by utilizing the geometry of these subspaces, and then encodes the model results onto individual images to infer their location. We report competitive results for location recognition and clustering on two public datasets, im2GPS and San Francisco, and empirically validate the utility of various design choices involved in the approach.

关键词： image recognition pattern clustering

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Learning Collections of Part Models for Object recognition

Learning Collections of Part Models for Object Recognition

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

作者： Endres, Ian Shih, Kevin J. Jiaa, Johnston Hoiem, Derek Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780769549897

We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. On PASCAL VOC 2010, we evaluate the part detectors' ability to discriminate and localize annotated keypoints. Our detection system is competitive with the best-existing systems, outperforming other HOG-based detectors on the more deformable categories.

关键词： learning (artificial intelligence) object detection object recognition

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