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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

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Efficient Regularized Least Squares Classification

Efficient Regularized Least Squares Classification

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Peng Zhang Jing Peng EECS Department Tulane University New Orleans LA USA

Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification. RLS minimizes a regularized functional directly in a reproducing kernel Hilbert space defined by a kernel. In contrast, support vector machines (SVMs) implement the structure risk minimization principle and use the kernel trick to extend it to the nonlinear case. While both have a sound mathematical foundation, RLS is strikingly simple. On the other hand, SVMs in general have a sparse representation of the solution. In this paper, we introduce a very fast version of the RLS algorithm while maintaining the achievable level of performance. The proposed new algorithm computes solutions in O(m) time and O(1) space, where m is the number of training points. We demonstrate the efficacy of our very fast RLS algorithm using a number of (both real simulated) data sets.

关键词： Least squares methods Resonance light scattering Kernel Hilbert space computer errors Support vector machines Support vector machine classification Risk management Equations Training data

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Visual Object Categorization using Distance-Based Discriminant Analysis

Visual Object Categorization using Distance-Based Discrimina...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Kosinov S. Marchand-Maillet T. Pun Computer Vision and Multimedia Laboratory University of Genova Geneva Switzerland

This paper formulates the problem of object categorization in the discriminant analysis framework focusing on transforming visual feature data so as to make it conform to the compactness hypothesis in order to improve categorization accuracy. The sought transformation, in turn, is found as a solution to an optimization problem formulated in terms of inter-observation distances only, using the technique of iterative majorization. The proposed approach is suitable for both binary and multiple-class categorization problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of discriminative features is determined automatically since the process of feature extraction is fully embedded in the optimization procedure. Performance tests validate our method on a number of benchmark data sets from the UCI repository, while the experiments in the application of visual object and content-based image categorization demonstrate very competitive results, asserting the method's capability of producing semantically relevant matches that share the same or synonymous vocabulary with the query category and allowing multiple pertinent category assignment.

关键词： Feature extraction computer vision Data mining Neural networks Benchmark testing Application software Vocabulary Focusing Object detection Linear discriminant analysis

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Stochastic Diffusion for Correspondence Estimation and Objects Segmentation

Stochastic Diffusion for Correspondence Estimation and Objec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sang Hwa Lee Nam Ik Cho Jong-Il Park School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea Division of Electrical and Computer Engineering Hanyang University Seoul South Korea

In this paper, a generative model combined with stochastic framework is proposed and applied to the simultaneous correspondence estimation and object segmentation. The correspondence and segment fields are explicitly modelled as Markov random fields, and estimated in the maximum a posteriori framework. Some stochastic models are defined as the potential functions to reflect the interaction of the fields. The potential functions of the fields are stochastically diffused with the probability distributions of the neighboring fields, and the probability spaces of the fields are updated from the diffused potential spaces. The stochastic diffusion proposed as an energy minimization process is a kind of generative model which updates and regenerates the probability spaces of the correspondence and segment fields. Some experiments are performed on the simultaneous correspondence estimation and object segmentation. The results show stable and good performances in estimating the correspondence fields and extracting the objects in the scene.

关键词： Stochastic processes Object segmentation Image segmentation Probability distribution Minimization methods Markov random fields Maximum a posteriori estimation Layout Image coding Video surveillance

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An Approach to Visualize Image Retrieval Results

An Approach to Visualize Image Retrieval Results

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A. Schneidewind P. Neumann I. Schmitt Faculty of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

Many current content-based image retrieval systems suffer from poor result presentation. A sophisticated visualization can be used to identify differences between human perception of similarity and system-determined similarity. Analyzing such discrepancies is a prerequisite for a system trimmed towards the user's comprehension of the underlying retrieval process. The aim of the visualization techniques presented in this paper is to provide a tool to analyze a mismatch between the user's perception and the system's calculation of similarity. We combine techniques of visual image retrieval and information visualization to acquire insight into the extracted feature data. In our project we implemented visualization techniques to present feature data on three different levels of abstraction. We discuss our experiences when working with a Data Table, a Parallel Coordinate Plot, and a Color Space Plot.

关键词： Image retrieval Data visualization Content based retrieval Humans Feature extraction Data mining Information retrieval Feedback Switches computer science

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Simultaneous Pose Estimation and Camera Calibration from Multiple Views

Simultaneous Pose Estimation and Camera Calibration from Mul...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： T. Izo W.E.L. Grimson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

We present an algorithm to estimate the body pose of a walking person given synchronized video input from multiple uncalibrated cameras. We construct an appearance model of human walking motion by generating examples from the space of body poses and camera locations, and clustering them using expectation-maximization. Given a segmented input video sequence, we find the closest matching appearance cluster for each silhouette and use the sequence of matched clusters to extrapolate the position of the camera with respect to the person's direction of motion. For each frame, the matching cluster also provides an estimate of the walking phase. We combine these estimates from all views and find the most likely sequence of walking poses using a cyclical, feed-forward hidden Markov model. Our algorithm requires no manual initialization and no prior knowledge about the locations of the cameras.

关键词： Cameras Calibration Legged locomotion Clustering algorithms Biological system modeling Humans Video sequences Phase estimation Feedforward systems Hidden Markov models

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Challenges in Segmentation of Human Forms in Outdoor Video

Challenges in Segmentation of Human Forms in Outdoor Video

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zongyi Liu S. Sarkar Computer Science and Engineering University of South Florida Tampa FL USA

Most gait and activity recognition algorithms rely on the use of silhouettes as the low-level representation. However, the detection of good silhouettes is still an open problem, particularly for sequences that are taken outdoors. Illumination conditions, compression artifacts, and low number of pixels on the subject, contribute to the difficulty. Presently, these issues are either ignored by using indoor data or addressed, on a case by case basis, by employing, essentially, a "bag of tricks" based approach. We argue for a more formal approach, based on generic shape and motion models to handle a variety of these problems, under the umbrella of one formalism. We present an HMM-based Eigen Stance model, built based on manually created silhouettes from 71 individuals. The population HMM helps map a frame in any given sequence to a stance and the appearance based Eigen-Stance model is used to reconstruct the computed silhouette in that frame. We quantify the performance in terms of signal based criteria of missed detection and false positive prediction rate. We also show results on three different databases.

关键词： Humans Hidden Markov models computer vision Image segmentation Lighting Shape Motion segmentation Cameras computer science Databases

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Learning a Factorized Segmental Representation of Far-Field Tracking Data

Learning a Factorized Segmental Representation of Far-Field ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： C. Stauffer Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

There are many useful observable characteristics of the state of a tracked object. These characteristics could include normalized size, normalized speed, normalized direction, object color, position, and object shape among other characteristics. Although these characteristics are by no means completely independent of each other, it is desirable to determine a separate, compact description of each of each of these aspects. Using this compact factored description, different aspects of individual sequences can be estimated and described without overwhelming computational or storage costs. In this work, we describe Factored Latent Analysis (FLA) and its application to deriving factored models for segmenting sequences in each of K separate characteristics. This method exploits temporally local statistics within each of the latent aspects and their inter-dependencies to derive a model that allows segmentation of each of the observed characteristics. This method is data driven and unsupervised. Activity classification results for multiple challenging environments are shown.

关键词： Shape Statistics Humans Legged locomotion Layout Graphical models Learning computer science Artificial intelligence Laboratories

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Slightly Supervised Learning of Part-Based Appearance Models

Slightly Supervised Learning of Part-Based Appearance Models

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Lexing Xie P. Perez Department of Electrical Engineering Columbia University New York NY USA Campus de Beaulieu Irisa/Inria Rennes France

We extend the GMM-based approach of [Selection of scale-invariant parts for object class recognition], for learning part-based appearance models of object categories, to the unsupervised case where positive examples are corrupted with clutter. To this end, we derive an original version of EM which is able to fit one GMM per class based on partially labeled data. We also allow ourselves a small fraction of un-corrupted positive examples, thus obtaining an effective, yet cheap, slightly supervised learning. Proposed technique allows as well a saliency-based ranking and selection of learnt mixture components. Experiments show that both the semi-supervised GMM fitting with side information and the component selection are effective in identifying salient patches in the appearance of a class of objects. They are thus promising tools to learn class-specific models and detectors similar to those by Weber et al.[Unsupervised learning of models for recognition], but at a lower computational cost, while accommodating larger numbers of atomic parts.

关键词： Supervised learning Detectors Image edge detection Computational efficiency Focusing Costs Unsupervised learning Context modeling Turning computer Society

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Graph Partitioning Active Contours for Knowledge-Based Geo-Spatial Segmentation

Graph Partitioning Active Contours for Knowledge-Based Geo-S...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： B. Sumengen S. Bhagavathy B.S. Manjunath Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

Our contribution in this paper is two-fold. First, we extend our previous curve evolution method based on pairwise similarities. This curve evolution equation combines the grouping abilities of active contours and graph partitioning techniques. Connections of our method to spectral graph partitioning are investigated and comparisons are made. Second, in a model-based segmentation scenario, we propose a method to improve segmentation quality by iteratively modifying the model using feedback from segmentation of a labeled training set. Our purpose here is to segment objects in geo-spatial images by integrating domain knowledge with the segmentation method. We achieve our goal by combining a statistical model for the object with a knowledge-guided segmentation method. Experimental results show that this framework is effective for model-based segmentation of complex geo-spatial objects.

关键词： Active contours Image segmentation Statistics Feedback Region 5 Histograms Energy measurement Partial differential equations Monitoring Minimization methods

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Object Class recognition with Many Local Features

Object Class Recognition with Many Local Features

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Helmer D.G. Lowe Department of Computer Science University of British Columbia Vancouver BC Canada

In this paper we present a method to recognize an object class by learning a statistical model of the class. The probabilistic model decomposes the appearance of an object class into a set of local parts and models the appearance, relative location, co-occurrence, and scale of these parts. However, in many object classification approaches that use local features, learning the parameters is exponential in the number of parts because of the problem of matching local features in the image to parts in the model. In this paper we present a learning method that overcomes this difficulty by adding new parts to the model incrementally, using the Maximum-Likelihood framework. When we add a part to the model, a set of candidate parts are selected and the part that increases the likelihood of the data the most is added to the model. Once this part is added to the model, the parameters for all parts up to this point are updated using EM. The learning and recognition in this approach are translation and scale invariant, robust to background clutter, and has less restriction on the number of parts in the model. The validity of the approach is demonstrated on a real world dataset, where the approach is competitive with others, and where the learning for a rich model is much faster than previous approaches.

关键词： Robustness Face recognition Image recognition Space exploration computer science Learning systems computer vision Motorcycles Shape

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