In this paper we propose an Isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. Being interested in user-independent head pose estimation, we extend the ...
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ISBN:
(纸本)0769521282
In this paper we propose an Isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. Being interested in user-independent head pose estimation, we extend the Isomap model [1] to be able to map (high-dimensional) input data points which are not in the training data set into the dimensionality-reduced space found by the model From this representation, a pose parameter map relating the input face samples to view angles is learnt. the proposed method is evaluated on a large database of multi-view face images in comparison to two other recently proposed subspace methods.
We propose an original learning approach for image classification problems. Recognizing semantic events in video requires to preliminary learn the different classes of events. this first stage is crucial since it cond...
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ISBN:
(纸本)0769521282
We propose an original learning approach for image classification problems. Recognizing semantic events in video requires to preliminary learn the different classes of events. this first stage is crucial since it conditions the further classification results. In video content analysis, the task is especially difficult due to the high intra-class variability and to noisy measurements. We then represent each class by the centers of several sub-classes (or clusters) thanks to a robust partitional clustering algorithm which can be applied in parallel to a (non-predefined) number of classes. Our clustering technique overcome three main limitations of standard K-means methods: sensitivity to initialization, choice of the number of clusters and influence of outliers. Moreover, it can process the training data in an incremental way. Experimental results on sports videos are reported.
Self-organizing neural networks achieve more predictable and accurate results then the classic ones withthe static architecture. Neurons and connections of such neural networks are dynamically built during the learni...
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ISBN:
(纸本)0769521282
Self-organizing neural networks achieve more predictable and accurate results then the classic ones withthe static architecture. Neurons and connections of such neural networks are dynamically built during the learning process. Self-organizing neural networks based on the Group Method of data Handling (GMDH) have proven to be one of the most efficient approaches to solving the problems of patternrecognition withthe statistical learningdata. In this article we propose a new method for searching deeper interrelations of the inputs and the output of the system under the study of such a neural network. the method allows eliminating links to the inputs that are no longer useful at the later steps of the neural network construction, thus allowing to simplify the neural network structure and increase prediction accuracy. Hence the method is called the Structure Relaxation Method. For complex problems the method helps to find deeper system inputs interrelations, increase the prediction accuracy, and, at the same time, decrease the number of the inputs being used. the proposed relaxation method was tested on the real world problems;the results are also presented herein.
K-Means clustering is a well-known partition-based technique in unsupervised learning to construct pattern models. the main difficulty, however, is that its performance is highly susceptible to the initialized partiti...
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ISBN:
(纸本)0769521282
K-Means clustering is a well-known partition-based technique in unsupervised learning to construct pattern models. the main difficulty, however, is that its performance is highly susceptible to the initialized partition. To attack this problem, a suboptimal K-Means algorithm is briefly reviewed by applying dynamic programming over the principal component direction. In particular, a heuristic clustering dissimilarity, the Delta-MSE function, is incorporated into the suboptimal K-Means algorithm. the Delta-MSE function is derived by calculating the difference of within-class variance before and after moving a given data sample from one cluster to another. Experimental results show that the suboptimal K-Means algorithm that uses the Delta-MSE dissimilarity generally outperforms the original L-2 distance based suboptimal algorithm and a specific kd-tree clustering algorithm.
We present a novel representation of cyclic human locomotion based on a set of spatio-temporal curves of tracked points on the surface of a person. We start by extracting a set of continuous, phase aligned spatio-temp...
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ISBN:
(纸本)0769521282
We present a novel representation of cyclic human locomotion based on a set of spatio-temporal curves of tracked points on the surface of a person. We start by extracting a set of continuous, phase aligned spatio-temporal curves from trajectories of random points tracked over several cycles of locomotion in a monocular video sequence. We analyze a PCA representation of a set of cyclic curves, pointing out properties of the representation which can be used for spatio-temporal alignment in tracking and recognition tasks. We model the curve distribution density by a mixture of Gaussians using expectation-maximization algorithm. For recognition, we use maximum a posteriori estimate combined with linear data adaptation. We tested the algorithms on CMU MoBo database with favourable results for the recognition of people "by walking" from monocular video sequences captured from the side view.
Various definitions and frameworks for discovering frequent trees in forests have been developed recently. At the heart of these frameworks lies the notion of matching, which determines when a pattern tree matches a t...
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ISBN:
(纸本)0769521428
Various definitions and frameworks for discovering frequent trees in forests have been developed recently. At the heart of these frameworks lies the notion of matching, which determines when a pattern tree matches a tree in a data set. We introduce a novel notion of tree matching for use in frequent tree mining and we show that it generalizes the framework of Zaki while still being more specific than that of Termier et al. Furthermore, we show how Zaki's TreeMinerV algorithm can be adapted towards our notion of tree matching. Experiments show the promise of the approach.
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners...
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ISBN:
(纸本)0769521428
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners and compare their effectiveness withthat of the newly developed Venn Probability machine (VPM) meta-learner [9]. We assess improvements in reliability using loss functions, Receiver Operator Characteristic (ROC) curves and Empirical Reliability Curves (ERC). the VPM outperforms the simple techniques to improve reliability, although at the cost of increased computational intensity and slight increase in error rate. these trade-offs are discussed.
the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online ro...
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ISBN:
(纸本)3540221174
the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online rough sets bibliography and accessible under the following web-site address: http://*** the service has been developed in order to facilitate the creation of rough sets bibliography, for various types of publications. At the moment the bibliography contains over 1400 entries from more than 450 authors. It is possible to create the bibliography in HTML or BibTeX format. In order to broaden the service contents it is possible to append new data using specially dedicated form. After appending data online the database is updated automatically. If one prefers sending a data file to the database administrator, please be aware that the database is updated once a month. In the current version of the RSDS system, there is the possibility for appending to each publication an abstract and keywords. As a natural consequence of this improvement there exists a possibility for searching a publication by keywords.
We describe here the three main platforms in the ERIM family of Web-based environments for human interpreting, two of them in more details, ERIM-Interp and ERIM-Collect, then ERIM-Aid. Each platform supports an aspect...
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ISBN:
(纸本)2951740816
We describe here the three main platforms in the ERIM family of Web-based environments for human interpreting, two of them in more details, ERIM-Interp and ERIM-Collect, then ERIM-Aid. Each platform supports an aspect of the collecting or study of spontaneous bilingual dialogues, translated by an interpreter. ERIM-Interp is the core environment, providing mediated communication between speakers and human interpreters over the network. Using ERIM-Collect, French-Chinese interpreting data have been collected within the 3-year "ChinFaDial" project supported by LIAMA, a French-Chinese laboratory in Beijing. these "raw" speech data will be made available in the spring of 2004 on an open-access basis, using the DistribDial server, on a CLIPSGETA website. Our goal is to extend such corpora, on a collaborative scheme, to allow other research groups to contribute to the site whatever annotations they may have created, and to share them under the same conditions (GPL). An ERIM-Aid variant is intended to provide focused machine AIDS to Web-based human interpreters, or to monolingual distant speakers conversing in different languages.
the proceedings contain 39 papers. the special focus in this conference is on Algorithms in Bioinformatics. the topics include: Reconstructing ancestral bacterial genomes from gene-content and order data;reconstructin...
ISBN:
(纸本)3540230181
the proceedings contain 39 papers. the special focus in this conference is on Algorithms in Bioinformatics. the topics include: Reconstructing ancestral bacterial genomes from gene-content and order data;reconstructing ancestral gene orders using conserved intervals;sorting by reversals with common intervals;a polynomial-time algorithm for the matching of crossing contact-map patterns;algorithms for finding maximal-scoring segment sets;gapped local similarity search with provable guarantees;suboptimal local alignments across multiple scoring schemes;adding hidden nodes to gene networks;joint analysis of DNA copy numbers and gene expression levels;searching for regulatory elements of alternative splicing events using phylogenetic footprinting;supervised learning-aided optimization of expert-driven functional protein sequence annotation;solving the protein threading problem by Lagrangian relaxation;recognition of similar spatial and chemical organizations;an algorithmic tool for domain discovery in protein sequences;sequence database compression for peptide identification from tandem mass spectra;a new integer programming formulation for the pure parsimony problem in haplotype analysis;a fast heuristic for single individual SNP haplotype reconstruction;the minisatellite transformation problem revisited;a faster and more space-efficient algorithm for inferring arc-annotations of RNA sequences through alignment;new algorithms for multiple DNA sequence alignment;chaining algorithms for alignment of draft sequence;translation initiation sites prediction with mixture Gaussian models;topological rearrangements and local search method for tandem duplication trees and integrating sample-driven and pattern-driven approaches in motif finding.
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