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Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing最新文献

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Independent component analysis for understanding multimedia content 用于理解多媒体内容的独立组件分析
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030096
T. Kolenda, L. K. Hansen, J. Larsen, O. Winther
{"title":"Independent component analysis for understanding multimedia content","authors":"T. Kolenda, L. K. Hansen, J. Larsen, O. Winther","doi":"10.1109/NNSP.2002.1030096","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030096","url":null,"abstract":"Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval classification rates increase if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised classifier which works from unsupervised ICA features is invoked. In addition, we demonstrate the suggested framework for automatic annotation of descriptive key words to images.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126139770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 64
A multi-channel recurrent network for synthesizing struck coupled-string musical instruments 用于合成敲击耦合弦乐器的多通道循环网络
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030079
Wei-Chen Chang, A. Su
{"title":"A multi-channel recurrent network for synthesizing struck coupled-string musical instruments","authors":"Wei-Chen Chang, A. Su","doi":"10.1109/NNSP.2002.1030079","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030079","url":null,"abstract":"Struck string instruments, such as pianos, usually have groups of strings with each group terminated at a common bridge. Because of the strong coupling phenomenon, the produced tones exhibit highly complex amplitude modulation patterns. Therefore, it is difficult to determine synthesis model parameters such that the synthesized tones can match recorded tones. A multi-channel recurrent network is proposed based on three previous works: the coupled-string model, the commuted piano synthesis method and the IIR synthesis method. This work attempts to extract automatically the synthesis parameters by using a neural-network training algorithm without the knowledge of the physical properties of the instruments. Computer simulations show encouraging results.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129238913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Adaptive BP neural network (ABPNN) based PN code acquisition system via recursive accumulator 基于递归累加器的自适应BP神经网络(ABPNN) PN码采集系统
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030092
Jiang-Yao Chen, Shun-Hsyung Chang, S. Leu
{"title":"Adaptive BP neural network (ABPNN) based PN code acquisition system via recursive accumulator","authors":"Jiang-Yao Chen, Shun-Hsyung Chang, S. Leu","doi":"10.1109/NNSP.2002.1030092","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030092","url":null,"abstract":"An adaptive back propagation (BP) neural network based PN code acquisition system is presented. Conventional neural network based acquisition systems are usually trained on PN code, but this system is based on training a back propagation neural network at all possible phases of the output of a correlation detector which is modified by a recursive accumulator. The recursive accumulator can converge the input of the neural network into a limited sample space, and the BP neural network acquires the phase of the received PN code from the converged data. The advantages of this system are that the gain of the system is controllable and the training data sample space is limited. The BP neural network is used to distinguish the transmitted signal and noise. Computer simulations show that the proposed system can acquire the phase of the received PN code correctly at very low signal-to-noise ratio (SNR) in an AWGN channel.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Minimum classification error via a Parzen window based estimate of the theoretical Bayes classification risk 通过基于理论贝叶斯分类风险估计的帕森窗口最小分类误差
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030053
E. McDermott, S. Katagiri
{"title":"Minimum classification error via a Parzen window based estimate of the theoretical Bayes classification risk","authors":"E. McDermott, S. Katagiri","doi":"10.1109/NNSP.2002.1030053","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030053","url":null,"abstract":"This article shows that the minimum classification error (MCE) criterion function commonly used for discriminative design of pattern recognition systems is equivalent to a Parzen window based estimate of the theoretical Bayes classification risk. In this analysis, each training token is mapped to the center of a Parzen kernel in the domain of a suitably defined \"output level\" random variable. The kernels are summed to produce a density estimate; this estimate in turn can easily be integrated over the domain of incorrect classifications, yielding the risk estimate. The expression of risk for each kernel can be seen to correspond directly to the usual MCE loss function. The resulting risk estimate can be minimized by suitable adaptation of the recognition system parameters that determine the mapping from training token to kernel center. This analysis provides a novel link between the MCE empirical cost measured on a finite training set and the theoretical Bayes classification risk.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126690117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Self-organizing map applied to image denoising 自组织映射在图像去噪中的应用
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030064
Michel Haritopoulos, Hujun Yin, N. Allinson
{"title":"Self-organizing map applied to image denoising","authors":"Michel Haritopoulos, Hujun Yin, N. Allinson","doi":"10.1109/NNSP.2002.1030064","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030064","url":null,"abstract":"We treat self-organizing maps (SOMs) as means for denoising of images corrupted by multiplicative noise. To achieve this goal, we propose a scheme for blind source separation based on a nonlinear topology preserving mapping as it is performed by SOMs. Despite the assumption that only two noisy frames of the same image scene are available, we show that by a suitable post-processing step based on the estimates provided by the SOM, one can obtain enhanced versions of the originally noisy scenes. Our work is illustrated by application results of the proposed method to test and real images.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126774594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Neural network-based segmentation of textures using Gabor features 基于Gabor特征的纹理神经网络分割
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030048
A. G. Ramakrishnan, S. Raja, H. Ram
{"title":"Neural network-based segmentation of textures using Gabor features","authors":"A. G. Ramakrishnan, S. Raja, H. Ram","doi":"10.1109/NNSP.2002.1030048","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030048","url":null,"abstract":"The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptron (MLP) architecture. The classification results obtained show an improvement over those obtained by K-means clustering and maximum likelihood approaches.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 27
MCMC joint separation and segmentation of hidden Markov fields 隐马尔可夫域的MCMC联合分离与分割
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030060
H. Snoussi, A. Mohammad-Djafari
{"title":"MCMC joint separation and segmentation of hidden Markov fields","authors":"H. Snoussi, A. Mohammad-Djafari","doi":"10.1109/NNSP.2002.1030060","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030060","url":null,"abstract":"We consider the problem of the blind separation of noisy instantaneously mixed images. The images are modelized by hidden Markov fields with unknown parameters. Given the observed images, we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov chain (MCMC) procedure. We separate the unknown variables into two categories: (1) the parameters of interest which are the mixing matrix, the noise covariance and the parameters of the sources distributions; and (2) the hidden variables which are the unobserved sources and the unobserved pixels classification labels. The proposed algorithm provides in the stationary regime samples drawn from the posterior distributions of all the variables involved in the problem leading to a flexibility in the cost function choice. We discuss and characterize some problems of non-identifiability and degeneracies of the parameters likelihood and the behavior of the MCMC algorithm in this case. Finally, we show the results for both synthetic and real data to illustrate the feasibility of the proposed solution.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128343937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Fusion of multiple experts in multimodal biometric personal identity verification systems 多模态生物识别个人身份验证系统中多专家的融合
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030012
J. Kittler, K. Messer
{"title":"Fusion of multiple experts in multimodal biometric personal identity verification systems","authors":"J. Kittler, K. Messer","doi":"10.1109/NNSP.2002.1030012","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030012","url":null,"abstract":"We investigate two trainable methods of classifier fusion in the context of multimodal personal identity verification involving eight experts which exploit voice characteristics and frontal face biometrics. As baseline classifier combination methods, simple fusion rules (Sum and Vote) which do not require any training are used. The results of experiments on the XM2VTS database show that all four combination methods investigated yield improved performance. Trainable fusion strategies do not appear to offer better performance than simple rules.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"349 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113966976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 37
A new SOLPN-based rate control algorithm for MPEG video coding 一种新的基于solpn的MPEG视频编码速率控制算法
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030069
Zhiming Zhang, Seung-Gi Chang, Jeonghoon Park, Yongje Kim
{"title":"A new SOLPN-based rate control algorithm for MPEG video coding","authors":"Zhiming Zhang, Seung-Gi Chang, Jeonghoon Park, Yongje Kim","doi":"10.1109/NNSP.2002.1030069","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030069","url":null,"abstract":"A new SOLPN (self-organizing learning Petri net)-based rate control algorithm for an MPEG encoder is proposed. The idea is to use SOLPN to realize the RD (rate distortion) model, which is self-organized on line and adaptively updated frame by frame. The method does not require off-line pre-training; hence it is geared toward real-time coding. The comparative results on the examples suggest that our proposed rate control schemes encode video sequences with fewer frame skips, providing good subjective quality and higher PSNR, compared to VM18.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114314503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Temporal associative memory and function approximation with the self-organizing map 时间联想记忆与自组织映射的功能逼近
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030022
G. Barreto, A. Araujo
{"title":"Temporal associative memory and function approximation with the self-organizing map","authors":"G. Barreto, A. Araujo","doi":"10.1109/NNSP.2002.1030022","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030022","url":null,"abstract":"We propose an unsupervised neural modelling technique, called vector-quantized temporal associative memory (VQTAM), which enables Kohonen's self-organizing map (SOM) to approximate nonlinear dynamical mappings globally. A theoretical analysis of the VQTAM scheme demonstrates that the approximation error decreases as the SOM training proceeds. The SOM is compared with standard MLP and RBF networks in the forward and inverse identification of a hydraulic actuator. The simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network; both the MLP and the RBF being supervised algorithms. The SOM is also less sensitive to weight initialization than MLP networks. The paper is concluded with a brief discussion on the main properties of the VQTAM approach.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133551065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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