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Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2008年第6期19卷 382-391页

作者： Shen, Shuhan Liu, Yuncai Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces;(b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return;(c) run with a high computation efficiency, run at about 20 frames/s. (C) 2008 Elsevier Inc. All rights reserved.

关键词： face tracking face detection multiple faces tracking real-time tracking probabilistic algorithms Relevance Vector Machine Boosting AdaBoost

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Secure reliable multicast protocols in a WAN

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DISTRIBUTED COMPUTING 2000年第1期13卷 19-28页

作者： Malkhi, D Merritt, M Rodeh, O AT&T Labs Res Shannon Lab Florham Park NJ 07932 USA Hebrew Univ Jerusalem Jerusalem Israel

A secure reliable multicast protocol enables a process to send a message to a group of recipients such that all correct destinations receive the same message, despite the malicious efforts of fewer than a third of the total number of processes, including the sender. This has been shown to be a useful tool in building secure distributed services, albeit with a cost that typically grows linearly with the size of the system. For very large networks, for which this is prohibitive, we present two approaches for reducing the cost: First, we show a protocol whose cost is on the order of the number of tolerated failures. Secondly, we show how relaxing the consistency requirement to a probabilistic guarantee can reduce the associated cost, effectively to a constant.

关键词： secure multicast probabilistic algorithms wide area network

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Predictive Ensemble Pruning by Expectation Propagation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2009年第7期21卷 999-1013页

作者： Chen, Huanhuan Tino, Peter Yao, Xin Univ Birmingham CERCIA Sch Comp Sci Birmingham B15 2TT W Midlands England

An ensemble is a group of learners that work together as a committee to solve a problem. The existing ensemble learning algorithms often generate unnecessarily large ensembles, which consume extra computational resource and may degrade the generalization performance. Ensemble pruning algorithms aim to find a good subset of ensemble members to constitute a small ensemble, which saves the computational resource and performs as well as, or better than, the unpruned ensemble. This paper introduces a probabilistic ensemble pruning algorithm by choosing a set of "sparse" combination weights, most of which are zeros, to prune the ensemble. In order to obtain the set of sparse combination weights and satisfy the nonnegative constraint of the combination weights, a left-truncated, nonnegative, Gaussian prior is adopted over every combination weight. Expectation propagation (EP) algorithm is employed to approximate the posterior estimation of the weight vector. The leave-one-out (LOO) error can be obtained as a by-product in the training of EP without extra computation and is a good indication for the generalization error. Therefore, the LOO error is used together with the Bayesian evidence for model selection in this algorithm. An empirical study on several regression and classification benchmark data sets shows that our algorithm utilizes far less component learners but performs as well as, or better than, the unpruned ensemble. Our results are very competitive compared with other ensemble pruning algorithms.

关键词： Machine learning probabilistic algorithms ensemble learning regression classification

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C⁴: Exploring Multiple Solutions in Graphical Models by Cluster Sampling

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2011年第9期33卷 1713-1727页

作者： Porway, Jake Zhu, Song-Chun New York Times Co R&D Div New York NY USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

This paper presents a novel Markov Chain Monte Carlo (MCMC) inference algorithm called C-4-Clustering with Cooperative and Competitive Constraints-for computing multiple solutions from posterior probabilities defined on graphical models, including Markov random fields (MRF), conditional random fields (CRF), and hierarchical models. The graphs may have both positive and negative edges for cooperative and competitive constraints. C-4 is a probabilistic clustering algorithm in the spirit of Swendsen-Wang [34]. By turning the positive edges on/off probabilistically, C-4 partitions the graph into a number of connected components (ccps) and each ccp is a coupled subsolution with nodes connected by positive edges. Then, by turning the negative edges on/off probabilistically, C-4 obtains composite ccps (called cccps) with competing ccps connected by negative edges. At each step, C-4 flips the labels of all nodes in a cccp so that nodes in each ccp keep the same label while different ccps are assigned different labels to observe both positive and negative constraints. Thus, the algorithm can jump between multiple competing solutions (or modes of the posterior probability) in a single or a few steps. It computes multiple distinct solutions to preserve the intrinsic ambiguities and avoids premature commitments to a single solution that may not be valid given later context. C-4 achieves a mixing rate faster than existing MCMC methods, such as various Gibbs samplers [15], [26] and Swendsen-Wang cuts [2], [34]. It is also more "dynamic" than common optimization methods such as ICM [3], LBP [21], [37], and graph cuts [4], [20]. We demonstrate the C4 algorithm in line drawing interpretation, scene labeling, and object recognition.

关键词： Markov random fields computer vision graph labeling probabilistic algorithms constraint satisfaction Monte Carlo

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Nested Monte Carlo EM algorithm for switching state-space models

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2005年第12期17卷 1653-1663页

作者： Popescu, CA Wong, YS Grant MacEwan Coll Edmonton AB T5J 4S2 Canada Univ Alberta Edmonton AB T6G 2G1 Canada

Switching state-space models have been widely used in many applications arising from science, engineering, economic, and medical research. In this paper, we present a Monte Carlo Expectation Maximization (MCEM) algorithm for learning the parameters and classifying the states of a state-space model with a Markov switching. A stochastic implementation based on the Gibbs sampler is introduced in the expectation step of the MCEM algorithm. We study the asymptotic properties of the proposed algorithm, and we also describe how a nesting approach and the Rao-Blackwellized forms can be employed to accelerate the rate of convergence of the MCEM algorithm. Finally, the performance and the effectiveness of the proposed method are demonstrated by applications to simulated and physiological experimental data.

关键词： time series analysis machine learning Markov processes Kalman filtering probabilistic algorithms parameter learning Monte Carlo simulation

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Shotgun Assembly of Labeled Graphs

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2019年第2期6卷 145-157页

作者： Mossel, Elchanan Ross, Nathan Univ Calif Berkeley Berkeley CA 94720 USA Univ Penn Philadelphia PA 19104 USA Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

We consider the problem of reconstructing graphs or labeled graphs from neighborhoods of a given radius r. Special instances of this problem include the well-known: DNA shotgun assembly;the lesser-known: neural network reconstruction;and a new problem: assembling random jigsaw puzzles. We provide some necessary and some sufficient conditions for correct recovery both in combinatorial terms and for some generative models including random labelings of lattices, Erdos-Renyi random graphs, and a random jigsaw puzzle model. Many open problems and conjectures are provided.

关键词： Random graphs statistical identifiability probabilistic algorithms shotgun assembly random jigsaw puzzles

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RANDOMNESS-OPTIMAL UNIQUE ELEMENT ISOLATION WITH APPLICATIONS TO PERFECT MATCHING AND RELATED PROBLEMS

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SIAM JOURNAL ON COMPUTING 1995年第5期24卷 1036-1050页

作者： CHARI, S ROHATGI, P SRINIVASAN, A CORNELL UNIV DEPT COMP SCI ITHACA NY 14853 USA

In this paper, we precisely characterize the randomness complexity of the unique element isolation problem, a crucial step in the RNC algorithm for perfect matching by Mulmuley, Vazirani, and Vazirani [Combinatorica, 7 (1987), pp. 105-113] and in several other applications. Given a set S and an unknown family F subset of or equal to 2(S) with \F\ less than or equal to Z, we present a scheme for assigning polynomially bounded wrights to the elements of S using only O(log Z + log \S\) random bits, such that the minimum weight set in F is unique with high probability. This generalizes the solution of Mulmuley, Vazirani, and Vazirani, who use O(S log S) bits, independent of Z. We also provide a matching lower bound for the randomness complexity of this problem. The new weight assignment scheme yields a randomness-efficient RNC(2) algorithm for perfect matching which uses O(log Z + log n) random bits, where Z is any given upper bound on the number of perfect matchings in the input graph. This generalizes the result of Grigoriev and Karpinski [Proc. IEEE Symposium on Foundations of Computer Science, 1987, pp. 166-172], who present an NC3 algorithm when Z is polynomial and improves the running time in this case. The worst-case randomness complexity of our algorithm is O(n log(m/n)) random bits improving on the previous bound of O(m log n). Our scheme also gives randomness-efficient solutions for several problems where unique element isolation is used, such as RNC algorithms for variants of matching and basic problems on linear matroids. We obtain a randomness-efficient random reduction from SAT to USAT, the language of uniquely satisfiable formulas, which can be derandomized in the case of languages in Few P to yield new proofs of the results Few P subset of or equal to + P and Few P subset of or equal to C = P.

关键词： PARALLEL algorithms probabilistic algorithms MATCHING MATROIDS RANDOM REDUCTIONS

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Ontology based approach to Bayesian student model design

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第13期40卷 5363-5371页

作者： Grubisic, Ani Stankov, Slavomir Peraic, Ivan Fac Sci Split Croatia High Sch Biograd Mora Biograd Na Moru 23210 Croatia

probabilistic student model based on Bayesian network enables making conclusions about the state of student's knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine "a priori" probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while "a priory" probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining "a priori" probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Intelligent tutoring systems e-learning Knowledge modeling probabilistic algorithms Bayesian network Conditional probabilities

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Model Fitting with Sufficient Random Sample Coverage

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2010年第1期89卷 120-128页

作者： Scherer-Negenborn, Norbert Schaefer, Rolf FGAN FOM Ettlingen Germany

It has been observed previously that the number of iterations required to derive good model parameter values used by RANSAC-like model estimators is too optimistic. We present the derivation of an analytical formula that allows the calculation of the sufficient limit of iterations needed to obtain good parameter values with the prescribed probability for any number of model parameters. It explains the values that had been found experimentally for certain numbers of model parameters by others very well. Furthermore, the improvement that our approach of SUfficient Random SAmple Coverage (SURSAC) offers, in comparison to the original RANSAC algorithm as well as to its adaptive modification by Hartley and Zisserman, is demonstrated with synthetic data for the case of a non-linear model function over a wide range of outlier fractions and different ratios of inlier and outlier densities.

关键词： Model fitting probabilistic algorithms RANSAC Robust regression

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A field model for human detection and tracking

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2006年第5期28卷 753-765页

作者： Wu, Y Yu, T Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the equilibrium of the field. It leads to computationally efficient methods for calculating the image likelihood and for training the model. Based on that, effective algorithms for detecting nonrigid objects are developed. This new approach has several advantages. First, it is intrinsically suitable for capturing local nonrigidity. In addition, due to the distributed likelihood, this approach is robust to partial occlusions. Moreover, the two-layer structure provides large flexibility of modeling the image observations, which makes the new method robust to clutters. Extensive experiments demonstrate its effectiveness.

关键词： object detection shape Markov random fields image models machine learning statistical computing probabilistic algorithms

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