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检索条件"机构=Statistical Visual Computing Laboratory"

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Boosting classifier cascades

Boosting classifier cascades

引用

24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Saberian, Mohammad J. Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The problem of optimal and automatic design of a detector cascade is considered. A novel mathematical model is introduced for a cascaded detector. This model is analytically tractable, leads to recursive computation, and accounts for both classification and complexity. A boosting algorithm, FCBoost, is proposed for fully automated cascade design. It exploits the new cascade model, minimizes a Lagrangian cost that accounts for both classification risk and complexity. It searches the space of cascade configurations to automatically determine the optimal number of stages and their predictors, and is compatible with bootstrapping of negative examples and cost sensitive learning. Experiments show that the resulting cascades have state-of-the-art performance in various computer vision problems.

关键词： Classification (of information)

来源：评论

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A biologically plausible network for the computation of orientation dominance

A biologically plausible network for the computation of orie...

引用

24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Muralidharan, Kritika Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781617823800

The determination of dominant orientation at a given image location is formulated as a decision-theoretic question. This leads to a novel measure for the dominance of a given orientation θ, which is similar to that used by SIFT. It is then shown that the new measure can be computed with a network that implements the sequence of operations of the standard neurophysiological model of V1. The measure can thus be seen as a biologically plausible version of SIFT, and is denoted as bioSIFT. The network units are shown to exhibit trademark properties of V1 neurons, such as cross-orientation suppression, sparseness and independence. The connection between SIFT and biological vision provides a justification for the success of SIFT-like features and reinforces the importance of contrast normalization in computer vision. We illustrate this by replacing the Gabor units of an HMAX network with the new bioSIFT units. This is shown to lead to significant gains for classification tasks, leading to state-of-the-art performance among biologically inspired network models and performance competitive with the best non-biological object recognition systems.

关键词： Object recognition

来源：评论

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Variable margin losses for classifier design 10

Variable margin losses for classifier design

引用

Annual Conference on Neural Information Processing Systems

作者： Hamed Masnadi-Shirazi Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego LaJollaCA 92039

ISBN: (纸本)9781617823800

The problem of controlling the margin of a classifier is studied. A detailed analytical study is presented on how properties of the classification risk, such as its optimal link and minimum risk functions, are related to the shape of the loss, and its margin enforcing properties. It is shown that for a class of risks, denoted canonical risks, asymptotic Bayes consistency is compatible with simple analytical relationships between these functions. These enable a precise characterization of the loss for a popular class of link functions. It is shown that, when the risk is in canonical form and the link is inverse sigmoidal, the margin properties of the loss are determined by a single parameter. Novel families of Bayes consistent loss functions, of variable margin, are derived. These families are then used to design boosting style algorithms with explicit control of the classification margin. The new algorithms generalize well established approaches, such as LogitBoost. Experimental results show that the proposed variable margin losses outperform the fixed margin counterparts used by existing algorithms. Finally, it is shown that best performance can be achieved by cross-validating the margin parameter.

关键词： Tumor margin status Classifiers Canonical form Variable

来源：评论

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Boosting Classifier Cascades 10

Boosting Classifier Cascades

引用

Annual Conference on Neural Information Processing Systems

作者： Mohammad J. Saberian Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego La Jolla CA 92039

ISBN: (纸本)9781617823800

The problem of optimal and automatic design of a detector cascade is considered. A novel mathematical model is introduced for a cascaded detector. This model is analytically tractable, leads to recursive computation, and accounts for both classification and complexity. A boosting algorithm, FCBoost, is proposed for fully automated cascade design. It exploits the new cascade model, minimizes a La-grangian cost that accounts for both classification risk and complexity. It searches the space of cascade configurations to automatically determine the optimal number of stages and their predictors, and is compatible with bootstrapping of negative examples and cost sensitive learning. Experiments show that the resulting cascades have state-of-the-art performance in various computer vision problems.

关键词： optimum number computer vision waterfalls Computer-Aided Design Boost bootstrapping cascade configuration Classifiers Optimal

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A biologically plausible network for the computation of orientation dominance 10

A biologically plausible network for the computation of orie...

引用

Annual Conference on Neural Information Processing Systems

作者： Kritika Muralidharan Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego La Jolla CA 92039

ISBN: (纸本)9781617823800

The determination of dominant orientation at a given image location is formulated as a decision-theoretic question. This leads to a novel measure for the dominance of a given orientation 9, which is similar to that used by SIFT. It is then shown that the new measure can be computed with a network that implements the sequence of operations of the standard neurophysiological model of V1. The measure can thus be seen as a biologically plausible version of SIFT, and is denoted as bioSIFT. The network units are shown to exhibit trademark properties of VI neurons, such as cross-orientation suppression, sparseness and independence. The connection between SIFT and biological vision provides a justification for the success of SIFT-like features and reinforces the importance of contrast normalization in computer vision. We illustrate this by replacing the Gabor units of an HMAX network with the new bioSIFT units. This is shown to lead to significant gains for classification tasks, leading to state-of-the-art performance among biologically inspired network models and performance competitive with the best non-biological object recognition systems.

关键词： Sifting network model dominance Reasoning computer vision Network

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On the design of loss functions for classification: Theory, robustness to outliers, and SavageBoost

On the design of loss functions for classification: Theory, ...

引用

22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781605609492

The machine learning problem of classifier design is studied from the perspective of probability elicitation, in statistics. This shows that the standard approach of proceeding from the specification of a loss, to the minimization of conditional risk is overly restrictive. It is shown that a better alternative is to start from the specification of a functional form for the minimum conditional risk, and derive the loss function. This has various consequences of practical interest, such as showing that 1) the widely adopted practice of relying on convex loss functions is unnecessary, and 2) many new losses can be derived for classification problems. These points are illustrated by the derivation of a new loss which is not convex, but does not compromise the computational tractability of classifier design, and is robust to the contamination of data with outliers. A new boosting algorithm, SavageBoost, is derived for the minimization of this loss. Experimental results show that it is indeed less sensitive to outliers than conventional methods, such as Ada, Real, or LogitBoost, and converges in fewer iterations.

关键词： Specifications

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On the design of loss functions for classification: theory, robustness to outliers, and SavageBoost 08

On the design of loss functions for classification: theory, ...

引用

Proceedings of the 22nd International Conference on Neural Information Processing Systems

作者： Hamed Masnadi-Shirazi Nuno Vasconcelos Statistical Visual Computing Laboratory University of California San Diego La Jolla CA

ISBN: (纸本)9781605609492

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Asymmetric boosting 07

Asymmetric boosting

引用

24th International Conference on Machine Learning, ICML 2007

作者： Masnadi-Shirazi, Hamed Vasconcelos, Nuno Statistical Visual Computing Laboratory University of California San Diego San Diego CA 92039 United States

ISBN: (纸本)9781595937933

A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic principles, which exploit the statistical interpretation of boosting to determine a principled extension of the boosting loss. Similarly to AdaBoost, the cost-sensitive extension minimizes this loss by gradient descent on the functional space of convex combinations of weak learners, and produces large margin detectors. It is shown that asymmetric boosting is fully compatible with AdaBoost, in the sense that it becomes the latter when errors are weighted equally. Experimental evidence is provided to demonstrate the claims of cost-sensitivity and large margin. The algorithm is also applied to the computer vision problem of face detection, where it is shown to outperform a number of previous heuristic proposals for cost-sensitive boosting (AdaCost, CSB0, CSB1, CSB2, asymmetric-AdaBoost, AdaC1, AdaC2 and AdaC3).

关键词： Decision theory

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Scalable discriminant feature selection for image retrieval and recognition

Scalable discriminant feature selection for image retrieval ...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： N. Vasconcelos M. Vasconcelos Statistical Visual Computing Laboratory University of California San Diego USA Harvard Robotics Laboratory Harvard University USA

Problems such as object recognition or image retrieval require feature selection (FS) algorithms that scale well enough to be applicable to databases containing large numbers of image classes and large amounts of data per class. We exploit recent connections between information theoretic feature selection and minimum Bayes error solutions to derive FS algorithms that are optimal in a discriminant sense without compromising scalability. We start by formalizing the intuition that optimal FS must favor discriminant features while penalizing discriminant features that are redundant. We then rely on this result to derive a new family of FS algorithms that enables an explicit trade-off between complexity and classification optimality. This trade-off is controlled by a parameter that encodes the order of feature redundancies that must be explicitly modeled to achieve the optimal solution. Experimental results on databases of natural images show that this order is usually low, enabling optimal FS with very low complexity.

关键词： Image retrieval Image recognition Scalability Laboratories Information retrieval Image databases Spatial databases Face detection Large-scale systems Biology computing

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