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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8679卷 190-197页

作者： Cheplygina, Veronika Tax, David M.J. Loog, Marco Feragen, Aasa Pattern Recognition Laboratory Delft University of Technology Netherlands The Image Group University of Copenhagen Denmark Machine Learning and Computational Biology Group Max Planck Institutes Tübingen Germany

We present an anatomically guided feature selection scheme for prediction of neurological disorders based on brain connectivity networks. Using anatomical information not only gives rise to an interpretable model, but also prevents overfitting, caused by high dimensionality, noise and correlated features. Our method selects meaningful and discriminative groups of connections between anatomical regions, which can be used as input for any supervised classifier, such as logistic regression or a support vector machine. We demonstrate the effectiveness of our method on a dataset of autism spectrum disorder, with an AUC of 0.76, outperforming baseline methods. © Springer International Publishing Switzerland 2014.

关键词： Feature Selection

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Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

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International Journal of Wireless and Mobile Computing 2015年第3期8卷 256-261页

作者： Wang, Zhonghua Liao, Yuan Liu, Qingping Li, Chunyong School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China Department of Information Engineering Nanchang Hangkong University Nanchang330063 China

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: (1) researching the image transformation technology from greyscale pattern to entropy pattern;(2) studying the anisotropic smoothing and local constraint criterion of entropy flow;and (3) introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Entropy

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Generalized quantum genetic algorithm of multidimensional space

Generalized quantum genetic algorithm of multidimensional sp...

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2014 International Conference on Logistics Engineering, Management and Computer Science, LEMCS 2014

作者： Zhang, Xiaofeng Miao, Wei Sui, Guifang Yang, Guowei Key Laboratory of JiangXi Procince for Image Processing and Pattern Recognition Information Engineering of NanChang HangKong University NanChang JiangXi330063 China

ISBN: (纸本)9789462520103

Quantum coding plays an important role in quantum genetic algorithm and affects the optimizing efficiency of algorithm, However, there are some defects in existing quantum genetic algorithm: the quantum coding scheme reduces the stability of the algorithm, making the algorithm easily occurs premature convergence and falls into local extremums. Therefore, coding scheme of multi gene chain is extended in a four dimensional space and multidimensional space based on an improved algorithm. Through simulation experiments of function extremum, combine double and triplestranded coding scheme, in accordance with the characteristics of qubits and the normalization condition. Through the experiment, study the effect of multiple genes encoding scheme chain on the performance of the algorithm. The results show that the optimal results of the algorithm can be better and the precision of solution can be higher by increasing the genetic chain. It is an effective strategy to improve the performance of algorithm. © 2014. The authors - Published by Atlantis Press.

关键词： Chains

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A Robust CPD Approach Based on Shape Context 33

A Robust CPD Approach Based on Shape Context

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第三十三届中国控制会议

作者： YANG Yawen ZHANG Peng Peng QIAO Yu YANG Jie WANG Sheng Zheng The Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University Merchant Marine College Shanghai Maritime University

ISBN: (纸本)9781479947249

Point matching is an important component of image *** years,Coherent Point Drift(CPD) method becomes a very popular point matching *** treats point matching as a probability estimation problem and speeds up the process of matching a *** this method,one set of points are thought to be sampled from a Gaussian Mixture Model(GMM),which is centered by the other set of ***,CPD is sensitive to outliers and noises,especially when the noise ratio increased or the number of outliers gets much *** deal with this problem,we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this *** main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid,this Gaussian component should be have a more influence to ***,we set prior probability of GMM with the similarity between GMM components and the data *** the computation of similarity is based on shape *** experiments on 2D and 3D images show that when noise ratio is low,our method performs as well as CPD does,but as the ratio increased,our method is more robust and satisfactory than CPD.

关键词： CPD,Shape context,image registration,Point matching

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Gait period detection based on layered coding of depth information

Gait period detection based on layered coding of depth infor...

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International Conference on Machine Tool Technology and Mechatronics Engineering, ICMTTME 2014

作者： Zhang, Hao Lin Ben, Xian Ye Zhang, Peng Liu, Tian Jiao Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Science and Engineering Shandong University Jinan China

ISBN: (纸本)9783038352464

Gait period detection, serving as a preprocessor for gait recognition, is commonly studied in the recent past. In this paper, we proposed a novel gait period detection method for depth gait video stream. The method introduces the concept of layered coding for depth images which decreases computational complexity. Furthermore, the extreme value of the sum of layered codes for gait sequence is utilized to judge the period endpoint, which is in accord with the naked-eye observation. In addition, gait recognition experiments on the TUM GAID database are conducted with the description of gait features of one single detected period by the proposed scheme using tensor representation. The high recognition accuracy verifies the effectiveness of the proposed depth gait period detection method. © (2014) Trans Tech Publications, Switzerland.

关键词： Codes (symbols)

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A Study of Deep Belief Network Based Chinese Speech Emotion recognition

A Study of Deep Belief Network Based Chinese Speech Emotion ...

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International Conference on Computational Intelligence and Security

作者： Bu Chen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781479974351

This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.

关键词： Speech Feature extraction Speech recognition Emotion recognition Support vector machines Training Speech processing

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Optimal classification of epileptic EEG signals using neural networks and harmony search methods

Journal of Software

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Journal of Software 2014年第1期9卷 230-239页

作者： Gao, Xiao-Zhi Wang, Jing Tanskanen, Jarno M. A. Bie, Rongfang Wang, Xiaolei Guo, Ping Zenger, Kai College of Information Engineering Shanghai Maritime University China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Laboratory of Image Processing and Pattern Recognition Beijing Normal University China Department of Biomedical Engineering Tampere University of Technology Finland School of Foundational Education Peking University Health Science Center China

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training. © 2014 ACADEMY PUBLISHER.

关键词： Optimization

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Adaptive regularization deconvolution extraction algorithm for spectral signal processing

Adaptive regularization deconvolution extraction algorithm f...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Jian Yu Ping Guo A-li Luo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China National Astronomical Observatories Chinese Academy of Sciences Beijing China

Deconvolution is known as an ill-posed problem. In order to solve such a problem, a regularization method is needed to constrain the solution space and find a plausible and stable solution. In practice, it is very computation intensive when using cross-validation method to select the regularization parameter. In this paper, we present an adaptive regularization method to find the optimal regularization parameter value and represent the trade-off between model fitness of the data and the smoothness of the extracted signal. Spectral signal extraction experimental results demonstrate that the time complexity the proposed method is much lower than the one without adaptive regularization and is convenient for users also. And quantitative performance analysis show that the proposed intelligent approach performs better than that of current deconvolution extraction method and other extraction method used in the Large Area Multi-Objects Fiber Spectroscopy Telescope spectral signal processing pipeline.

关键词： Deconvolution Charge coupled devices Equations Apertures Convolution Mathematical model Sparse matrices

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Semi-supervised Fisher-Markov Selector via Manifold Regularization

Semi-supervised Fisher-Markov Selector via Manifold Regulari...

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2012 International Conference on Applied Informatics and Communication(ICAIC 2012)

作者： Dong Chen Lin Zhu JieYang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.

关键词： Feature selection, Dimensionality reduction, Manifold regularization, Semi-supervised learning

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Implicitly Constrained Semi-supervised Linear Discriminant Analysis

Implicitly Constrained Semi-supervised Linear Discriminant A...

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International Conference on pattern recognition

作者： Jesse H. Krijthe Marco Loog Department of Molecular Epidemiology Leiden University Medical Center The Netherlands Pattern Recognition Laboratory Delft University of Technology The Netherlands The Image Group Department of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9781479952106

Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-supervised variants have been proposed. Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account. In this work we compare traditional Expectation Maximization type approaches for semi-supervised linear discriminant analysis with approaches based on intrinsic constraints and propose a new principled approach for semi-supervised linear discriminant analysis, using so-called implicit constraints. We explore the relationships between these methods and consider the question if and in what sense we can expect improvement in performance over the supervised procedure. The constraint based approaches are more robust to misspecification of the model, and may outperform alternatives that make more assumptions on the data in terms of the log-likelihood of unseen objects.

关键词： Linear discriminant analysis Covariance matrices Mathematical model Semisupervised learning Equations Labeling pattern recognition

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