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Margin-Based Discriminant Dimensionality Reduction for Visual recognition

Margin-Based Discriminant Dimensionality Reduction for Visua...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Hakan Cevikalp Frederic Jurie Bill Triggs Robi Polikar Eskisehir Osmangazi University Eskisehir Turkey Laboratoire Jean Kuntzmann Grenoble France University of Caen France Rowan University Glassboro NJ USA

Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the class regions densely. In such cases, test samples often fall into gaps between training samples where the nearest neighbours are too distant to be good indicators of class membership. One solution is to project the data onto a discriminative lower dimensional subspace. We propose a gap-resistant nonparametric method for finding such subspaces: first the gaps are filled by building a convex model of the region spanned by each class - we test the affine and convex hulls and the bounding disk of the class training samples - then a set of highly discriminative directions is found by building and decomposing a scatter matrix of weighted displacement vectors from training examples to nearby rival class regions. the weights are chosen to focus attention on narrow margin cases while still allowing more diversity and hence more discriminability than the 1D linear Support Vector Machine (SVM) projection. Experimental results on several face and object recognition datasets show that the method finds effective projections, allowing simple classifiers such as nearest neighbours to work well in the low dimensional reduced space.

关键词： Testing Scattering Kernel Matrix decomposition Support vector machines Support vector machine classification Face recognition Object recognition Multidimensional systems Filling

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Conditional Density Learning via Regression with Application to Deformable Shape Segmentation

Conditional Density Learning via Regression with Application...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Jingdan Zhang Shaohua Kevin Zhou Dorin Comaniciu Leonard McMillan Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA Department of Computer Science University of North Carolina Chapel Hill Chapel Hill NC USA

Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C|I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities of the model. We propose to learn a conditional probability density satisfying these desired properties for the given training data set. To do this, we formulate a novel regression problem that finds a function approximating the target density. Learning the regressor is challenging due to the high dimensionality of model parameters, C, and the complexity of relating the image and the model. Our approach makes two contributions. First, we take a multi-level refinement approach by learning a series of density functions, each of which guides the solution of optimization algorithms increasingly converging to the correct solution. Second, we propose a new data sampling algorithm that takes into account the gradient information of the target function. We have applied this learning approach to deformable shape segmentation and have achieved better accuracy than the previous methods.

关键词： Image segmentation Application software Density functional theory Image converters Training data Facial features Active shape model Data systems computer science Probability density function

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Human Action recognition using Local Spatio-Temporal Discriminant Embedding

Human Action Recognition using Local Spatio-Temporal Discrim...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Kui Jia Dit-Yan Yeung Shenzhen Institute of Advanced Integration Technology CAS/CUHK Shenzhen China Hong Kong University of Science and Technology Hong Kong China

Human action video sequences can be considered as nonlinear dynamic shape manifolds in the space of image frames. In this paper, we address learning and classifying human actions on embedded low-dimensional manifolds. We propose a novel manifold embedding method, called Local Spatio-Temporal Discriminant Embedding (LSTDE). the discriminating capabilities of the proposed method are two-fold: (1) for local spatial discrimination, LSTDE projects data points (silhouette-based image frames of human action sequences) in a local neighborhood into the embedding space where data points of the same action class are close while those of different classes are far apart;(2) in such a local neighborhood, each data point has an associated short video segment, which forms a local temporal subspace on the embedded manifold. LSTDE finds an optimal embedding which maximizes the principal angles between those temporal subspaces associated with data points of different classes. Benefiting from the joint spatio-temporal discriminant embedding, our method is potentially more powerful for classifying human actions with similar space-time shapes, and is able to perform recognition on a frame-by-frame or short video segment basis. Experimental results demonstrate that our method can accurately recognize human actions, and can improve the recognition performance over some representative manifold embedding methods, especially on highly confusing human action types.

关键词： Humans Shape Space technology Video sequences Learning systems Information analysis computer vision Optical computing Image recognition Content addressable storage

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Learning Object Motion patterns for Anomaly Detection and Improved Object Detection

Learning Object Motion Patterns for Anomaly Detection and Im...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Arslan Basharat Alexei Gritai Mubarak Shah Computer Vision Lab School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. the proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach.

关键词： Object detection Motion detection Layout Surveillance Feedback Cameras Pipelines Event detection Probability density function Videos

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Max Margin AND/OR Graph Learning for Parsing the Human Body

Max Margin AND/OR Graph Learning for Parsing the Human Body

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Long (Leo) Zhu Yuanhao Chen Yifei Lu Chenxi Lin Alan Yuille Department of Statistics University of California Los Angeles USA University of Science and Technology China Shanghai Jiaotong University China Microsoft Research Asia China Department of Statistics Psychology and Computer Science University of California Los Angeles USA

We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human body and its parts by an AND/OR graph, which is a multi-level mixture of Markov Random Fields (MRFs). Max-margin learning, which is a generalization of the training algorithm for support vector machines (SVMs), is used to learn the parameters of the AND/OR graph model discriminatively. there are four advantages from this combination of AND/OR graphs and max-margin learning. Firstly, the AND/OR graph allows us to handle enormous articulated poses with a compact graphical model. Secondly, max-margin learning has more discriminative power than the traditional maximum likelihood approach. thirdly, the parameters of the AND/OR graph model are optimized globally. In particular, the weights of the appearance model for individual nodes and the relative importance of spatial relationships between nodes are learnt simultaneously. Finally, the kernel trick can be used to handle high dimensional features and to enable complex similarity measure of shapes. We perform comparison experiments on the baseball datasets, showing significant improvements over state of the art methods.

关键词： Humans Biological system modeling Machine learning Tree graphs Statistics Maximum likelihood estimation Kernel Shape measurement Performance analysis computer vision

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Principled Fusion of High-level Model and Low-level Cues for Motion Segmentation

Principled Fusion of High-level Model and Low-level Cues for...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Arasanathan thayananthan Masahiro Iwasaki Roberto Cipolla Department of Engineering University of Cambridge Cambridge UK Panasonic Europe Limited Cambridge UK

High-level generative models provide elegant descriptions of videos and are commonly used as the inference framework in many unsupervised motion segmentation schemes. However, approximate inference in these models often require ad-hoc initialization to avoid local minima issues. Low-level cues, obtained independently from the high-level model, can constrain the search space and reduce the chance of inference algorithms falling into a local minima. this paper introduces a novel principled fusion framework where, local hierarchical superpixels segmentation of images are used to capture local motion. the low-level cues such as local motion, on their own, not adequate to obtain full motion segmentation as occlusion needs to be handled globally. We fuse the low-level motion cues with the high-level model in a principled manner to surmount the shortcomings of using only the high-level model or low-level cues to perform motion segmentation. the fused model contains both continuous and discrete variables which forms a number of Markov Random fields. Variational approximation or belief propagation algorithms cannot be applied due to the complex interactions between the variables. Hence, approximate inference is performed using expectation propagation (EP) algorithm. the scheme is demonstrated by performing motion segmentation in two video sequences.

关键词： Motion segmentation computer vision Inference algorithms Fusion power generation Videos Image segmentation Fuses Markov random fields Belief propagation Approximation algorithms

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Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform

Spatio-temporal Saliency Detection Using Phase Spectrum of Q...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Chenlei Guo Qi Ma Liming Zhang Department of Electronic Engineering Fudan University Shanghai China

Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), Neuromorphic vision Toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. the results are good, but the reason is questionable. In this paper, we propose it is the phase spectrum, not the amplitude spectrum, of the Fourier transform that is the key in obtaining the location of salient areas. We provide some examples to show that PFT can get better results in comparison with SR and requires less computational complexity as well. Furthermore, PFT can be easily extended from a two-dimensional Fourier transform to a Quaternion Fourier Transform (QFT) if the value of each pixel is represented as a quaternion composed of intensity, color and motion feature. the added motion dimension allows the phase spectrum to represent spatio-temporal saliency in order to engage in attention selection for videos as well as images. Extensive tests of videos, natural images and psychological patterns show that the proposed method is more effective than other models. Moreover, it is very robust against white-colored noise and meets the real-time requirements, which has great potentials in engineering applications.

关键词： Phase detection Quaternions Fourier transforms Eyes Strontium Layout Humans Object detection computer vision Computational modeling

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Exact Inference in Multi-label CRFs with Higher Order Cliques

Exact Inference in Multi-label CRFs with Higher Order Clique...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Srikumar Ramalingam Pushmeet Kohli Karteek Alahari Philip H. S. Torr Oxford Brookes University UK Microsoft Research Limited Cambridge UK

this paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order boolean functions (referred to as (F{sub}s){sup}2), which can be minimized exactly using graph cuts and we characterize those classes. the basic idea is to use two or more boolean variables to encode the states of a single multi-label variable. there are many ways in which this can be done and much interesting research lies in finding ways which are optimal or minimal in some sense. We study the space of possible encodings and find the ones that can transform the most general class of functions to (F{sub}s){sup}2. Our main contributions are two-fold. First, we extend the subclass of submodular energy functions that can be minimized exactly using graph cuts. Second, we show how higher order potentials can be used to improve single view 3D reconstruction results. We believe that our work on exact minimization of higher order energy functions will lead to similar improvements in solutions of other labelling problems.

关键词： Labeling Boolean functions computer vision Polynomials Cost function Encoding Minimization methods Discrete transforms Image reconstruction Image segmentation

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Finding Trails

Finding Trails

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Scott Morris Kobus Barnard Computer Science Department University of Arizona Tucson USA

We present a statistical learning approach for finding recreational trails in aerial images. While the problem of recognizing relatively straight and well defined roadways in digital images has been well studied in the literature, the more difficult problem of extracting trails has received no attention. However, trails and rough roads are less likely to be adequately mapped, and change more rapidly over time. Automated tools for finding trails will be useful to cartographers, recreational users and governments. In addition, the methods developed here are applicable to the more general problem of finding linear structure. Our approach combines local estimates for image pixel trail probabilities with the global constraint that such pixels must link together to form a path. For the local part, we present results using three classification techniques. To construct a global solution (a trail) from these probabilities, we propose a global cost function that includes both global probability and path length. We show that the addition of a length term significantly improves trail finding ability. However, computing the optimal trail becomes intractable as known dynamic programming methods do not apply. thus we describe a new splitting heuristic based on Dijkstra's algorithm. We then further improve upon the results with a trail sampling scheme. We test our approach on 500 challenging images along the 2500 mile continental divide mountain bike trail, where assumptions prevalent in the road literature are violated.

关键词： Roads Pixel Statistical learning Image recognition Digital images Government Cost function Dynamic programming Image sampling Testing

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3D Face Tracking and Expression Inference from a 2D Sequence Using Manifold Learning

3D Face Tracking and Expression Inference from a 2D Sequence...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Wei-Kai Liao Gerard Medioni Computer Science Department Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. the rigid and nonrigid motions are analyzed simultaneously in 3D, by automatically fitting and tracking a set of landmarks. We do not represent all nonrigid facial deformations as a simple complex manifold, but instead decompose them on a basis of eight 1D manifolds. Each 1D manifold is learned offline from sequences of labeled expressions, such as smile, surprise, etc. Any expression is then a linear combination of values along these 8 axes, with coefficient representing the level of activation. We experimentally verify that expressions can indeed be represented this way, and that individual manifolds are indeed 1D. the manifold dimensionality estimation, manifold learning, and manifold traversal operation are all implemented in the N-D Tensor Voting framework. Using simple local operations, this framework gives an estimate of the tangent and normal spaces at every sample, and provides excellent robustness to noise and outliers. the output of our system, besides the tracked landmarks in 3D, is a labeled annotation of the expression. We demonstrate results on a number of challenging sequences.

关键词： Deformable models Active appearance model Active shape model Face detection Intelligent robots Facial animation Humans Principal component analysis computer science Robotics and automation

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