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Modified student's t-hidden Markov model for pattern recognition and classification

IET SIGNAL PROCESSING 2013年第3期7卷 219-227页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications;however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixture distribution as its hidden state is the Student's t-hidden Markov model (SHMM). The authors propose a novel Student's t-hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation-maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.

关键词： modified student t-hidden Markov model latent components expectation-maximisation algorithm optimal weight parameters t-mixture distribution Optimisation techniques pattern classification applications gradient method standard SHMM pattern classification synthetic data gradient methods emission densities Gaussian mixture models pattern recognition applications real data Gaussian processes hidden Markov models pattern recognition scalar exponent regularization component densities Markov processes

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Accurate and fast Jacobi-Fourier moments for invariant image recognition

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OPTIK 2016年第19期127卷 7925-7940页

作者： Upneja, Rahul Sri Guru Granth Sahib World Univ Dept Math Fatehgarh Sahib India Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

The Jacobi-Fourier moments (JFMs) which are useful for many image processing, pattern recognition and computer vision applications provide a wide class of orthogonal rotation invariant moments (ORIMs). The accuracy of JFMs suffers from various errors, such as the geometric error, numerical integration error, and discretization error. Moreover, the high order moments are vulnerable to numerical instability. In this paper, we present a fast method for the accurate calculation of JFMs which not only removes the geometric error and numerical integration error, but also provides numerical stability to JFMs of high orders. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Jacobi-Fourier moments Recursive method Numerical stability Accurate moments pattern recognition applications

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Application of empirical Bayes adaptive estimation technique for estimating winds from MST radar covering higher altitudes

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第7期17卷 3303-3311页

作者： Padhy, Manas Ranjan Vigneshwari, Srinivasan Ratnam, M. Venkat Sathyabama Inst Sci & Technol Chennai India Natl Atmospher Res Lab Gadanki India

Most of the works on estimating winds from the Doppler information provided by the very high frequency (VHF) radars use either parametric or nonparametric techniques. The studies using nonparametric signal processing techniques have tried to interpret the radar returns from higher altitudes where the signal to noise (SNR) is weak. It may be challenging to cover higher altitudes accurately without actually boosting signal strength by increasing transmit power and processing gain. The studies using parametric techniques have assumed data from a particular model to carry out pattern recognition and ascertain accuracy for a single frame of data, which does not guarantee consistency for an extended dataset. The present study introduces the empirical Bayesian approach for processing the radar data in near real-time to achieve higher altitude coverage. The eBayes adaptive estimation technique is a data-aware technique that learns from informative prior. It implements a hybrid model for data analytics, incorporating a nonparametric model to improve signal strength and a parametric model to reduce noise variance. It derives wind accurately from the Doppler, which is cross-validated with the wind information obtained from several GPS radiosonde simultaneous observations. In addition, the eBayes approach incorporates a modified version of classical moments estimation and introduces new quality parameters to quantify SNR characteristics. The study concludes that the eBayes analytical approach can accurately derive 3D winds consistently, covering higher altitudes of 25.2-28.8 km, improving the typical MST (Mesosphere Stratosphere and Troposphere) radar reference range of 21 km. This approach is helpful for several scientific investigations using measurements from VHF radars.

关键词： Adaptive estimation Empirical Bayes MST radar pattern recognition applications Signal detection Spectral analysis Statistical signal processing

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Active learning combining uncertainty and diversity for multi-class image classification

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IET COMPUTER VISION 2015年第3期9卷 400-407页

作者： Gu, Yingjie Jin, Zhong Chiu, Steve C. Nanjing Univ Sci & Technol Dept Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Idaho State Univ Dept Elect Engn Pocatello ID 83209 USA

In computer vision and pattern recognition applications, there are usually a vast number of unlabelled data whereas the labelled data are very limited. Active learning is a kind of method that selects the most representative or informative examples for labelling and training;thus, the best prediction accuracy can be achieved. A novel active learning algorithm is proposed here based on one-versus-one strategy support vector machine (SVM) to solve multi-class image classification. A new uncertainty measure is proposed based on some binary SVM classifiers and some of the most uncertain examples are selected from SVM output. To ensure that the selected examples are diverse from each other, Gaussian kernel is adopted to measure the similarity between any two examples. From the previous selected examples, a batch of diverse and uncertain examples are selected by the dynamic programming method for labelling. The experimental results on two datasets demonstrate the effectiveness of the proposed algorithm.

关键词： Knowledge engineering techniques Optimisation techniques learning (artificial intelligence) image classification Computer vision and image processing techniques support vector machine pattern recognition applications Image recognition support vector machines binary SVM classiflers multiclass image classification unlabelled data computer vision active learning algorithm dynamic programming method Gaussian kernel dynamic programming

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Turn-intent analysis using body pose for intelligent driver assistance

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IEEE PERVASIVE COMPUTING 2006年第4期5卷 28-37页

作者： Cheng, Shinko Yuanhsien Trivedi, Mohan Manubhai Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92037 USA

Pervasive computing technologies hold much promise for enhancing automotive safety by introducing a new range of human-centered driver assistance systems. Requirements for designing an active safety system are accurately, reliably, and quickly identifying the conditions leading to an accident and inducing corrective actions to prevent the accident. The authors propose a driver turn-maneuver prediction system using a two-class pattern classification algorithm using driver-pose and steering-angle information. They analyze classifier-detection performance using receiver-operator-characteristic curves. These curves provide a picture of the attainable proactivity versus transparency ratios, pertaining to a pervasive computing system's ability to foresee the user's needs as compared to the system's ability not to annoy the user. The goal is to motivate the development of both vision-based body-pose recovery and behavior recognition algorithms for driver assistance systems. This article is part of a special issue on Intelligent Transportation.

关键词： Pervasive Computing Computer Vision Transportation Face And Gesture recognition pattern recognition applications

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Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular-based tensor locality preservative projections

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IET COMPUTER VISION 2016年第7期10卷 670-678页

作者： Rajeshwari, Janthakal Karibasappa, Kwadiki Gopalkrishna, Madigondanahalli Thimmaiah Dayananda Sagar Coll Engn Dept Informat Sci Bangalore 560078 Karnataka India Oxford Coll Engn Bangalore 560061 Karnataka India KS Sch Engn & Management Dept Comp Sci Bangalore 560078 Karnataka India

Automatic face detection is a challenging task for computer vision and pattern recognition applications such as video surveillance and traffic monitoring. During the last few years, subspace methods have been proposed for visual learning and recognition which are sensitive to variations in illumination, pose and occlusion. To overcome these problems, the authors have proposed a method that combines block-based tensor locality preservative projection (TLPP) with Adaboost algorithm which improves the accuracy of face detection. In the proposed algorithm Adaboost modular TLPPs (AMTLPPs), the face image is divided into overlapping small blocks and these block features are given to TLPP to extract the features where TLPP take data directly in the form of tensors as input. AMTLPP algorithm selects the optimal block features from the large set of the block features which forms the weak classifiers and are combined to form the strong classifier. A number of assessments are conducted for YouTube celebrity, McGill face dataset and also on collected video sequences of an own dataset recorded under indoor, outdoor, day, sunset and crowded environment. Experimental results show that the proposed approach is effective and efficient.

关键词： pose variation McGill face dataset tensors Video signal processing Image recognition automatic face detection visual learning image sequences YouTube celebrity block-based TLPP algorithm block-based tensor locality preservative projection face recognition Algebra pattern recognition applications subspace method strong classifier computer vision image classification feature extraction illumination variation AMTLPP algorithm learning (artificial intelligence) video signal processing occlusion variation Knowledge engineering techniques weak classifier video sequence collection Adaboost modular-based tensor locality preservative projection

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Rule extraction from support vector machines: A sequential covering approach

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2007年第6期19卷 729-741页

作者： Barakat, Nahla H. Bradley, Andrew P. Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia

In this paper, we propose a novel algorithm for rule extraction from support vector machines ( SVMs), termed SQRex-SVM. The proposed method extracts rules directly from the support vectors ( SVs) of a trained SVM using a modified sequential covering algorithm. Rules are generated based on an ordered search of the most discriminative features, as measured by interclass separation. Rule performance is then evaluated using measured rates of true and false positives and the area under the receiver operating characteristic ( ROC) curve ( AUC). Results are presented on a number of commonly used data sets that show the rules produced by SQRex-SVM exhibit both improved generalization performance and smaller more comprehensible rule sets compared to both other SVM rule extraction techniques and direct rule learning techniques.

关键词： artificial intelligence machine learning representations information extraction pattern recognition applications SVMs

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Rule extraction from support vector machines A review

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NEUROCOMPUTING 2010年第1-3期74卷 178-190页

作者： Barakat, Nahla Bradley, Andrew P. German Univ Technol Oman Dept Appl Informat Technol Muscat Oman Univ Queensland Sch Informat Technol & Elect Engn ITEE St Lucia Qld 4072 Australia

Over the last decade support vector machine classifiers (SVMs) have demonstrated superior generalization performance to many other classification techniques in a variety of application areas However SVMs have an inability to provide an explanation or comprehensible justification for the solutions they reach It has been shown that the black-box nature of techniques like artificial neural networks (ANNs) is one of the main obstacles impeding their practical application Therefore techniques for rule extraction from ANNs and recently from SVMs were introduced to ameliorate this problem and aid in the explanation of their classification decisions In this paper we conduct a formal review of the area of rule extraction from SVMs The review provides a historical perspective for this area of research and conceptually groups and analyzes the various techniques In particular we propose two alternative groupings the first is based on the SVM (model) components utilized for rule extraction while the second is based on the rule extraction approach The aim is to provide a better understanding of the topic in addition to summarizing the main features of individual algorithms The analysis is then followed by a comparative evaluation of the algorithms salient features and relative performance as measured by a number of metrics It is concluded that there is no one algorithm that can be favored in general However methods that are kernel independent produce the most comprehensible rule set and have the highest fidelity to the SVM should be preferred In addition a specific method can be preferred if the context of the requirements of a specific application so that appropriate tradeoffs may be made The paper concludes by highlighting potential research directions such as the need for rule extraction methods in the case of SVM incremental and active learning and other application domains where special types of SVMs are utilized (C) 2010 Elsevier B V All rights reserved

关键词： Machine learning Data mining Knowledge discovery Information extraction pattern recognition applications SVMs

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On Some Scientific Results of the IMTA-VIII-2022: 8th International Workshop "Image Mining: Theory and applications"

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pattern recognition AND IMAGE ANALYSIS 2022年第3期32卷 460-465页

作者： Gurevich, Igor B. Moroni, Davide Pascali, Maria Antonietta Yashina, Vera V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Natl Res Council Italy Inst Informat Sci & Technol A Faedo Res Area Pisa I-56124 Pisa Italy

The publication presents an introductory paper to the Special issue of the international journal pattern recognition and Image Analysis: Advances in Mathematical Theory and applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented.

关键词： IMTA-8-2022 image mining image analysis mathematical theory of image analysis pattern recognition artificial intelligence automation data mining data science knowledge engineering application problems image analysis applications pattern recognition applications

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Evacuation Evaluation Using Machine Learning and Wearable Sensors

Evacuation Evaluation Using Machine Learning and Wearable Se...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Huang, D. Aida, S. Yoshikawa, A. Kitazawa, M. Terano, T. Takahashi, S. Takahashi, T. B. Tokyo Inst Technol Sch Comp Yokohama Kanagawa Japan Tokyo Univ Sci Sch Management Tokyo Japan Tokyo Univ Sci Dept Management Sci Tokyo Japan

ISBN: (纸本)9781538626528

This research proposes a method of evacuation evaluation using machine learning techniques. Evacuation evaluation must identify when and where people tend to make abnormal movement in emergency evacuation situations. Conventional method asks plenty of manpower to detect abnormal movement time periods. Our method detects the time periods automatically, using classifier and heuristics about human movement.

关键词： pattern recognition applications Machine learning applications Machine learning technologies

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