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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tejash Patel Robi Polikar Christos Davatzikos Christopher M. Clark Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.

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Incremental non-negative matrix factorization for dynamic background modelling

Incremental non-negative matrix factorization for dynamic ba...

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7th International Workshop on pattern recognition in Information Systems PRIS 2007;In Conjunction with ICEIS 2007

作者： Bucak, Serhat S. Gunsel, Bilge Gursoy, Ozan Multimedia Signal Processing and Pattern Recognition Lab. Dept. of Electronics and Comm. Eng. Istanbul Technical University 34469 Maslak Istanbul Turkey

ISBN: (纸本)9789728865931

In this paper, an incremental algorithm which is derived from Non-negative Matrix Factorization (NMF) is proposed for background modeling in surveillance type of video sequences. The adopted algorithm, which is called as Incremental NMF (INMF), is capable of modeling dynamic content of the surveillance video and controlling contribution of the subsequent observations to the existing representation properly. INMF preserves additive, parts-based representation, and dimension reduction capability of NMF without increasing the computational load. Test results are reported to compare background modeling performances of batch-mode and incremental NMF in surveillance type of video. Moreover, test results obtained by the incremental PCA are also given for comparison purposes. It is shown that INMF outperforms the conventional batch-mode NMF in all aspects of dynamic background modeling. Although object tracking performance of INMF and the incremental PCA are comparable, INMF is much more robust to illumination changes.

关键词： Non-negative matrix factorization

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Video Content Representation by Incremental Non-Negative Matrix Factorization

Video Content Representation by Incremental Non-Negative Mat...

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IEEE International Conference on Image processing

作者： Serhat S. Bucak Bilge Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Det. of Electronics and Communication Engineering Istanbul Technical University Istanbul Turkey

Nonnegative matrix factorization (NMF) is a powerful decomposition tool which has been used in several content representation applications recently. However, there are some difficulties in implementing NMF in on-line video applications. This paper introduces an incremental NMF (INMF) without deviating from conventional NMF"s main objective function, which is minimizing the reconstruction error. The proposed algorithm is capable of modeling dynamic content of the video; thus controls contribution of the subsequent observations to the NMF representation properly. It is shown that the INMF preserves additive, parts-based representation capability of the NMF with a low computational load while offering dimension reduction. Experimental results are given to compare the reconstruction performances of the conventional and incremental NMF. In addition, video scene change detection and dynamic video content representation by INMF are investigated. Test results demonstrate that the INMF can be used as a powerful on-line factorization tool.

关键词： Matrix decomposition Layout Testing

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MULTIPLE CLASSIFIERS BASED INCREMENTAL LEARNING ALGORITHM FOR LEARNING IN NONSTATIONARY ENVIRONMENTS

MULTIPLE CLASSIFIERS BASED INCREMENTAL LEARNING ALGORITHM FO...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： MICHAEL D.MUHLBAIER ROBI POLIKAR Signal Processing and Pattern Recognition Laboratory Rowan University Glassboro NJ 08028 USA

We describe an incremental learning algorithm designed to learn in challenging non-stationary environments, where the underlying data distribution that governs the classification problem changes at an unknown *** algorithm is based on a multiple classifier system that generates a new classifier every time a new dataset becomes available from the changing *** consider the particularly challenging form of this problem, where we assume that the previously generated data points are no longer available, even if some of those points may still be relevant in the new *** algorithm employs a strategic weighting mechanism to determine the error of each classifier on the current data distribution, and then combines the classifiers using a dynamically weighted majority *** describe the implementation details of algorithm, and track its performance as a function of the environment's rate of *** show that the algorithm is able to track the changing environment, even when the environment changes drastically over a short period of time.

关键词： Incremental learning Non-stationary learning Learn++ Ensemble systems Multiple classifier systems

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Random Feature Subset Selection for Analysis of Data with Missing Features

Random Feature Subset Selection for Analysis of Data with Mi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Joseph DePasquale Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Rowan University Glassboro NJ USA

We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn ++ . The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn from an iteratively updated distribution. To classify an instance with missing features, only those classifiers whose training data did not include the currently missing feature are used. These classifiers are combined by using a majority voting combination rule to obtain the final classification of the given instance. We had previously presented preliminary results on a similar approach, which could handle up to 10% missing data. In this study, we expand our work to include different types of rules to update the distribution, and also examine the effect of the algorithm's primary free parameter (the number of features used to train the ensemble of classifiers) on the overall classification performance. We show that this algorithm can now accommodate up to 30% of features missing without a significant drop in performance.

关键词： Data analysis Neural networks Training data Clustering algorithms Fuzzy neural networks USA Councils Algorithm design and analysis Iterative algorithms Random number generation Voting

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Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition

Maximum likelihood estimation of a reverberation model for r...

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European signal processing Conference (EUSIPCO)

作者： Armin Sehr Yuanhang Zheng Elmar Nöth Walter Kellermann Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Pattern Recognition (Informatik 5) University of Erlangen-Nuremberg Erlangen Germany

We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to find the means and variances of the reverberation model. In contrast to [1] and to HMM training on artificially reverberated training data (e. g. [2]), measurements of room impulse responses become unnecessary, and the effort for training is greatly reduced. Simulations of a connected digit recognition task show that, in highly reverberant environments, the reverberation models estimated by the proposed approach achieve significantly higher recognition rates than HMMs trained on reverberant data.

关键词： Hidden Markov models Reverberation Speech Speech recognition Maximum likelihood estimation Training Data models

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Perceptual Contrast-Based Image Fusion: A Variational Approach

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自动化学报 2007年第2期33卷 132-137页

作者： WANG Chao YE Zhong-Fu Institute of Statistical Signal Processing Department of Electrical Engineering and Information Science University of Science and Technology of China Hefei 230027 P.R.China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 P.R.China

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份... 详细信息

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份报纸考虑人的视觉系统(HVS ) 的性质并且从每幅来源图象把量的感性的变化转移到结果。把 just-noticeable-difference (JND ) 用作测量， multiband 图象的感性的对比作为一个目标被获得。我们构造一个功能的极值问题发现一幅单身的乐队图象，或熔化结果，它有最近感性的对比到目标一个。经由变化途径， Euler-Lagrange 方程被导出，并且一次坡度降下重复被采用。试验性的结果证明这个方法感性地好。

关键词：表象聚变变化方法知觉衬比度 HVS PDES 人工智能

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Ensemble Based Data Fusion from Parietal Region Event Related Potentials for Early Diagnosis of Alzheimer's Disease

Ensemble Based Data Fusion from Parietal Region Event Relate...

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International Joint Conference on Neural Networks (IJCNN)

作者： Brian A. Balut Matthew T. Karnick Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.

关键词： Alzheimer's disease Enterprise resource planning Hospitals Electrodes Senior citizens Biomarkers Protocols Delay Neural networks USA Councils

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Robust Audio Watermark Decoding by Supervised Learning

Robust Audio Watermark Decoding by Supervised Learning

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： S. Kirbiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communications Eng. Istanbul Technical University Istanbul Turkey

Most of the watermark (WM) decoding schemes use correlation-based methods because of their simplicity. In these methods, the WM signal embedded through a secret key is assumed as uncorrelated with the host signal. This is a hard restriction that can never be achieved and correlation between the received signal and the secret key becomes greater than zero even though the received signal is un-watermarked. Mostly a decision threshold specified semi-automatically is used at the decoding site. Since the audio watermarking is a nonlinear process that guarantees the inaudibility, there is no analytic way of determining an optimal threshold value that makes the WM decoding problem harder. This paper introduces a learning scheme followed by a nonlinear classification thus eliminates the threshold specification problem. The decoding process is modelled as a three-class classification problem and support vector machines (SVMs) are used in the learning of the embedded data. The decoding and detection performances of the developed system are greater than 98% and 95%, respectively. When the watermark-to-signal-ratio (WSR) is higher than -30 dB, system false alarm ratios remain less than 2%. It is shown that the introduced WM decoding method is robust to additive noise and most of add/remove and filter attacks of Stirmark

关键词： Watermarking Decoding Supervised learning Support vector machines Support vector machine classification Machine learning Meteorological radar Noise robustness Additive noise Filters

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Robust audio watermark decoding by nonlinear classification

Robust audio watermark decoding by nonlinear classification

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13th European signal processing Conference, EUSIPCO 2005

作者： Kirbiz, S. Yaslan, Y. Günsel, B. Multimedia Signal Processing and Pattern Recognition Lab. Dept. of Electronics and Communications Eng. Istanbul Technical University 34469 Istanbul Turkey

ISBN: (纸本)1604238216

This paper introduces an audio watermark (WM) decoding scheme that performs a Support Vector Machine (SVM) based supervised learning followed by a blind decoding. The decoding process is modelled as a two-class classification procedure. Initially, wavelet decomposition is performed on the training audio signals, and the decomposed audio frames watermarked with +1 and -1 constitute the training sets for Class 1 and Class 2, respectively. The developed system enables to extract embedded WM data at lower than -40dB Watermark-to-signal- Ratio (WSR) levels with more than 95% accuracy and it is robust to degradations including audio compression (MP3, AAC), and additive noise. It is shown that the proposed audio WM decoder eliminates the drawbacks of correlation-based methods.

关键词： Additive noise

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