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machine learning for Vision-based Motion Analysis

2010年

作者： Wang, Liang Zhao, Guoying Cheng, Li

ISBN: (纸本)9780857290564

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second international workshop on machine learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; and, investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engine

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Research and Improvement on Bintree Multi-class Categorization Algorithm Based on SVM

Research and Improvement on Bintree Multi-class Categorizati...

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1st international workshop on Database Technology and Applications

作者： Hu Yan Li Min Xiong Hao-yong Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9780769536040

It's a hotspot to expend the research on support vector machine from a two-class issue to a multi-class one. Among all kinds of methods, Bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting then others, and it works out the impartibility problem. So it is a good method. The dissertation systematically researches and analyses Bintree multi-class text categorization algorithm based on support vector machine, and improves it. That is, assemble first, and then sort them when the size of testing texts is too large. The aim is that after improvement the judgment of the testing text does not have to begin from the base crunode of Bintree, instead the testing text can be put into category function to be computed The improvement can enhance the efficiency of text categorization and the probability of accurate categorization when the size of testing texts is too big and the quantity of sorted functions is too large.

关键词： Support Vector machine Categorization Algorithm statistical learning Theory Quadratic Programming

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Data Prediction in Manufacturing: An Improved Approach Using Least Squares Support Vector machines

Data Prediction in Manufacturing: An Improved Approach Using...

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international workshop on Database Technology and Applications

作者： Zaifei Liao Tian Yang Xinjie Lu Hongan Wang Institute of Software Chinese Academy and Sciences Beijing China Chinese Acad. of Sci. Grad. Univ. Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy and Sciences Beijing China

Support vector machine (SVM) is a set of related supervised learning methods used for classification and regression based on statistical learning theory. In this paper, we present a least squares support vector machines (LSSVM) regression method based on relative error for manufacturing industries to estimate the true value of imprecise measured data during production logistics process. Our method has already been successfully applied in Manufacturing Execution System (MES) of some petrochemical enterprises in China.

关键词： Manufacturing Least squares methods Support vector machines Support vector machine classification Supervised learning statistical learning Least squares approximation Production Logistics Petrochemicals

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Automatic low-dimensional analysis of audio databases

Automatic low-dimensional analysis of audio databases

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international workshop on Content-Based Multimedia Indexing

作者： Arias, Jose Anibal Andre-Obrecht, Regine Farinas, Jerome Univ Toulouse 3 IRIT SAMOVA F-31062 Toulouse France

ISBN: (纸本)9781424420438

In this paper we present an approach designed to map variable size audio sequences into fixed-length vectors, useful to discover contents of audio databases. first, we model standard audio parameters with Gaussian mixture models (GMM). Then, symmetric Kullback-Leiber divergences between models are approximated with a Monte-Carlo method. We use these statistical dissimilarities to find a low-dimensional representation of each audio sequence through Multidimensional scaling (MDS) algorithm. Vectors in low-dimensional spaces are then easily explored with kernel and clustering methods. Experiments carried out in different kind of audio databases (music, speakers and languages) show good potential of the proposed approach and provide a framework for more challenging applications.

关键词： unsupervised learning audio databases machine learning

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Automatic low-dimensional analysis of audio databases

Automatic low-dimensional analysis of audio databases

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international workshop on Content-Based Multimedia Indexing, CBMI

作者： Jose Anibal Arias Regine Andre-Obrecht Jerome Farinas IRIT SAMOVA Universite Paul Sabatier Toulouse Toulouse France

关键词： Data analysis Audio databases Kernel Hidden Markov models Natural languages machine learning Feature extraction Multidimensional systems machine learning algorithms Spatial databases

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Efficient data selection for machine translation

Efficient data selection for machine translation

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IEEE Spoken Language Technology workshop

作者： A. Mandal D. Vergyri W. Wang J. Zheng A. Stolcke G. Tur D. Hakkani-Tur N. F. Ayan Speech Technology and Research Laboratory SRI International Inc. Menlo Park CA USA International Computer Science Institute Berkeley CA USA

Performance of statistical machine translation (SMT) systems relies on the availability of a large parallel corpus which is used to estimate translation probabilities. However, the generation of such corpus is a long and expensive process. In this paper, we introduce two methods for efficient selection of training data to be translated by humans. Our methods are motivated by active learning and aim to choose new data that adds maximal information to the currently available data pool. The first method uses a measure of disagreement between multiple SMT systems, whereas the second uses a perplexity criterion. We performed experiments on Chinese-English data in multiple domains and test sets. Our results show that we can select only one-fifth of the additional training data and achieve similar or better translation performance, compared to that of using all available data.

关键词： Surface-mount technology Training data Humans Probability Information retrieval Availability Natural languages Web pages System testing Speech

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deterministic and statistical methods in machine learning - first international workshop, Revised Lectures

Deterministic and Statistical Methods in Machine Learning - ...

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first international workshop on deterministic and statistical methods in machine learning

ISBN: (纸本)3540290737

The proceedings contain 19 papers. The topics discussed include: object recognition via local patch labelling;multi channel sequence processing;Bayesian Kernel learning methods for parametric accelerated life survival analysis;extensions of the informative vector machine;efficient communication by breathing;guiding local regression using visualisation;transformations of Gaussian process priors;redundant bit vectors for quickly searching high-dimensional regions;Bayesian independent component analysis with prior constraints: an application in biosignal analysis;ensemble algorithms for feature selection;understanding Gaussian process regression using the equivalent Kernel;and support vector machine to synthesise Kernels.

关键词： statistical methods

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Bayesian methods for multimedia signal processing

Bayesian methods for multimedia signal processing

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15th ACM international Conference on Multimedia, MM'07

作者： Cemgil, A. Taylan University of Cambridge Signal Processing and Communications Lab. Department of Engineering Trumpington Street CB2 1PZ Cambridge United Kingdom

ISBN: (纸本)9781595937025

In the last years, there have been a significant growth of multimedia information processing applications that employ ideas from statistical machine learning and probabilistic modeling. In this paradigm, multimedia data (music, audio, video, images, text, ...) are viewed as realizations from highly structured stochastic processes. Once a model is constructed, several interesting problems such as transcription, coding, classification, restoration, tracking, source separation or resynthesis etc. can be formulated as Bayesian inference problems. In this context, graphical models provide a "language" to construct models for quantification of prior knowledge. Unknown parameters in this specification are estimated by probabilistic inference. Often, however, the problem size poses an important challenge and in order to render the approach feasible, specialized inference methods need to be tailored to improve the computational speed and efficiency. The scope of the proposed tutorial is as follows: first, we will review the fundamentals of probabilistic models, with some focus on music, video and text data. Then, we will discuss the numerical techniques for inference in these models. In particular, we will review exact inference, approximate stochastic inference techniques such as Markov Chain Monte Carlo, Sequential Monte Carlo and deterministic (variational) inference techniques. Our ultimate aim is to provide a basic understanding of probabilistic modeling for multimedia processing, associated computational techniques and a roadmap such that information retrieval researchers new to the Bayesian approach can orient themselves in the relevant literature and understand the current state of the art.

关键词： Bayesian networks

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Data mining of RNA expression and DNA genotype data: Presentation group 5 contributions to Genetic Analysis workshop 15

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GENETIC EPIDEMIOLOGY 2007年第S1期31卷 S43-S50页

作者： Falk, Catherine T. Finch, Stephen J. Kim, Wonkuk Mukhopadhyay, Nitai D. Virginia Commonwealth Univ Richmond VA USA Univ S Florida Tampa FL USA

The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.

关键词： neural networks support vector machines Bayesian networks network construction multistage analysis mixture modeling

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Data mining, neural nets, trees -: Problems 2 and 3 of Genetic Analysis workshop 15

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GENETIC EPIDEMIOLOGY 2007年第S1期31卷 S51-S60页

作者： Ziegler, Andreas DeStefano, Anita L. Koenig, Inke R. Univ Lubeck Inst Med Biomet & Stat Univ Klinikum Schleswig Holstein D-23538 Lubeck Germany Boston Univ Sch Publ Hlth Dept Biostat Boston MA 02215 USA Boston Univ Sch Med Dept Neurol Boston MA 02215 USA

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

关键词： association analysis classification tree genome-wide analysis GWA logic regression logistic regression logistic trees neural net random forest single nucleotide polymorphism SNP

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