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SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category recognition

SVM-KNN: Discriminative Nearest Neighbor Classification for ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hao Zhang A.C. Berg M. Maire J. Malik Computer Science Division EECS Department University of California Berkeley CA USA

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.

关键词： Nearest neighbor searches Shape Humans Support vector machines Prototypes Support vector machine classification computer vision pattern recognition computer science Sampling methods

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A New Formulation for Shape from Shading for Non-Lambertian Surfaces

A New Formulation for Shape from Shading for Non-Lambertian ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： A.H. Ahmed A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

Lambert’s model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert’s model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are presented to show the efficiency of the proposed method . To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.

关键词： Shape Reflectivity Cameras Lighting Brightness Optical imaging Light sources Rough surfaces Surface roughness computer vision

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Planar-to-Curved-Surface Image Registration

Planar-to-Curved-Surface Image Registration

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： S. Gefen N. Kiryati L. Bertrand J. Nissanov College of Medicine Philadelphia PA USA Electrical Engineering School Tel-Aviv University Ramat-Aviv Israel

A 2D-to-3D non-rigid intensity-based registration method is proposed. This planar-to-curved-surface image alignment algorithm matches experimental data of twodimensional images with their corresponding images overlaid on a curved-surface within a volumetric image. This PDE-based 2D-to-3D registration technique allows for inter-modality matching of sectional data with a volumetric image of homologous objects. We demonstrate the feasibility of this method through simulations and in the application of atlas-based segmentation of a mouse brain histological and MRI images

关键词： Image registration Brain Educational institutions Biomedical imaging USA Councils Equations Mice Rail to rail inputs computer vision pattern recognition

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Context and Hierarchy in a Probabilistic Image Model

Context and Hierarchy in a Probabilistic Image Model

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conference on computer vision and pattern recognition (CVPR)

作者： Ya Jin S. Geman Division of Applied Mathematics Brown University USA

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a "composition machine") for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a "Markov backbone."

关键词： Context modeling Layout computer vision Biological system modeling Mathematics Licenses Bayesian methods Machine vision Mathematical model Airports

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Meta-Evaluation of Image Segmentation Using Machine Learning

Meta-Evaluation of Image Segmentation Using Machine Learning

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conference on computer vision and pattern recognition (CVPR)

作者： Hui Zhang S. Cholleti S.A. Goldman J.E. Fritts Department of Computer Science and Engineering Washington University Saint Louis MO USA Department of Mathematics and Computer Science Saint Louis University Saint Louis MO USA

Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.

关键词： Image segmentation Machine learning Application software computer vision Humans computer science Machine learning algorithms Drives Mathematics Training data

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Using Spatio-Temporal Patches for Simultaneous Estimation of Edge Strength, Orientation, and Motion

Using Spatio-Temporal Patches for Simultaneous Estimation of...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： A.N. Stein M. Hebert The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

We describe an extension to ordinary patch-based edge detection in images using spatio-temporal volumetric patches from video. The inclusion of temporal information enables us to estimate motion normal to edges in addition to edge strength and spatial orientation. The method can handle complex edges in clutter by comparing distributions of data on either half of an extracted patch, rather than modeling the intensity profile of the edge. An efficient approach is provided for building the necessary histograms which samples candidate edge orientations and motions. Results are compared to classical spatio-temporal filtering techniques.

关键词： Motion estimation Image edge detection Filters Filtering Layout computer vision Data mining Histograms Object recognition Detectors

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How Do Movie Viewers Perceive Scene Structure from Dynamic Cues

How Do Movie Viewers Perceive Scene Structure from Dynamic C...

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conference on computer vision and pattern recognition (CVPR)

作者： Loong-Fah Cheong Xu Xiang Department of Electrical and Computer Engineering National University of Singapore Singapore

Cinema viewed from a location other than a Canonical Viewing Point (CVP) presents distortions to the viewer in both its static and dynamic aspects. Past works have investigated mainly the static aspect of this problem and attempted to explain why viewers still seem to perceive the scene very well. The dynamic aspect of depth perception, which is known as structure from motion, and its possible distortion, have not been well investigated. In this paper, we derive the dynamic depth cues perceived by the viewer and use the so-called isodistortion framework to understand its distortion. The result is that viewers seated at a reasonably central position experience a shift in the intrinsic parameters of their visual systems. Despite this shift, the key properties of the perceived depths remain largely the same, being determined in the main by the accuracy to which extrinsic motion parameters can be recovered. For a viewer seated at a non-central position and watching the movie screen with a slant angle, the view is related to the view at the CVP by a homography, resulting in various aberrations such as non-central projection.

关键词： Motion pictures Layout Retina Shape computer errors Motion analysis Motion estimation Drives Visual system Eyes

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Salient Contour Detection using a Global Contour Discontinuity Measurement

Salient Contour Detection using a Global Contour Discontinui...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hongzhi Wang J. Oliensis Stevens Institute of Technology USA

Salient contour grouping/detection is one of the major topics in perceptual organization exploring the true meanings for contours. Extending contour’s discontinuity quality from low level discontinuity, we introduce a new perspective that a salient contour represents a sharp change in the ability to organize the image into meaningful parts. This new view gives a novel, general edge energy measure, which has important perceptual meanings. A new multiscale saliency function and its variational solution are also described. Experiments on real images validate our method.

关键词： Image edge detection Energy measurement Image segmentation Statistics Statistical distributions Shape Detectors Length measurement Gaussian processes computer vision

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Estimating Intrinsic Component Images using Non-Linear Regression

Estimating Intrinsic Component Images using Non-Linear Regre...

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conference on computer vision and pattern recognition (CVPR)

作者： M.F. Tappen E.H. Adelson W.T. Freeman MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA

Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art.

关键词： Layout Noise reduction Shape Surface treatment computer science Artificial intelligence Laboratories Image denoising Noise level Lighting

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Accurate Tracking of Monotonically Advancing Fronts

Accurate Tracking of Monotonically Advancing Fronts

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conference on computer vision and pattern recognition (CVPR)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.

关键词： computer vision Shape Finite difference methods Application software Computational complexity Nonlinear equations Partial differential equations Image processing Laboratories Grid computing

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