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...
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.
In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations ...
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In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. the possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted. Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.
In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) [26] for light field stereo matching to handle significant occlusions. the concept of SCam is used to model angular radiance dist...
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ISBN:
(纸本)9781479951192
In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) [26] for light field stereo matching to handle significant occlusions. the concept of SCam is used to model angular radiance distribution with respect to a 3D point. Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams. We further show how to distinguish between on-surface and free space, textured and non-textured, and Lambertian and specular through bilateral SCam analysis. To speed up the matching process, we apply the edge-preserving guided filter [14] on the consistency-disparity curves. Experimental results show that our technique outperforms boththe state-of-the-art and the recent light field stereo matching methods, especially near occlusion boundaries.
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 a...
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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 withpatterns 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.
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computervision. It, however faces the major difficulty to incorporate robust functions, rather than the square data fit...
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ISBN:
(纸本)9780769549897
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computervision. 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.
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-cla...
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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.
Large-scale recognition problems withthousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. the label tree model integrates classifi...
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ISBN:
(纸本)9780769549897
Large-scale recognition problems withthousands 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 withthe 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.
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, whe...
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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 computervision problems that can be expressed with polynomial energies, and demonstrate the benefits of our approach over existing continuous inference methods.
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 feat...
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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.
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning appr...
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ISBN:
(纸本)9780769549897
Recently active learning has attracted a lot of attention in computervision 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 computervision 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 computervision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
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