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Semi-supervised Learning for Automatic Audio Events Annotation Using TSVM

Semi-supervised Learning for Automatic Audio Events Annotati...

The 2010 International Conference on Computer Application and system Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Rongyan Wang Gang Liu Jun Guo Zhenxin Ma Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9781424472352

Most previous approaches to automatic audio events (Aes) annotation are based on supervised learning which relies on the availability of a labeled corpus to train classification models. However, instance annotation is often difficult, expensive, and time consuming. In this paper, we apply semi-supervised learning with transductive Support Vector Machine (TSVM) algorithm to automatic Aes annotation. Besides, considering about the presence of outliers which degrade the generalization and the classification performance, we propose a confidence-based method for samples selection. In our experiments based on the melodrama Friends corpus, the proposed method can effectively use unlabeled data to improve the classification performance with only a small amount of the labeled data.

关键词： audio events (Aes) TSVM semi-supervised learning sample selection

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Association pattern recognization by simulation between performance and faults in MAN

Association pattern recognization by simulation between perf...

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International Asia Conference on Informatics in Control, Automation and Robotics, CAR

作者： Fengxin Jin Bin Zhang Guohui Li Jin Zhou Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China China Research Laboratory IBM Beijing China

ISBN: (纸本)9781424451920

At present, network services have covered complex applications and value-added services, and service providers also need to consider service quality for customers' satisfaction. The equipment performance and faults directly affect the quality of service. Therefore, analyzing the relationship of service performance (the traffic distribution, transmission delay etc.), the network performance and faults based on some typical applications is of great significance. This paper builds typical MAN (metropolitan area network) topologies and fault scenarios which carrying VoIP application. Through simulation, performance data collection and mining, the association between network performance and faults will be found as the important foundation of association recognition between network faults and service quality.

关键词： pattern recognition Fault detection Large-scale systems intelligent networks Robotics and automation intelligent robots Performance analysis Telecommunication traffic Traffic control Monitoring

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Matching Image with Multiple Local Features

Matching Image with Multiple Local Features

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International Conference on pattern recognition

作者： Yudong Cao Honggang Zhang Yanyan Gao Xiaojun Xu Jun Guo Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Liaoning University of Technology Jinzhou China

In this paper, we present the fusional feature composed of Affine-SIFT, MSER and color moment invariants. The fusional feature is more robust and distinctive than a single local feature. Instead of adding three local features together simply, an efficient two-level matching strategy is devised with the fusional feature, which speeds up the establishment of the local correspondences. To remove partial false positives, an affine transformation is estimated with the weighted RANSAC which decreases iteration times. The experimental results show that our approach can achieve more accurate correspondence. We prospect to apply the fusional feature and match strategy to image retrieval in the end.

关键词： Geometry pattern recognition Image color analysis Nearest neighbor searches Robustness Computer vision Stereo vision

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Novel confidence feature extraction algorithm based on latent topic similarity

Novel confidence feature extraction algorithm based on laten...

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作者： Chen, Wei Liu, Gang Guo, Jun Omachi, Shinichiro Omachi, Masako Guo, Yujing Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing 100876 China Graduate School of Engineering Tohoku University Sendai-shi 980-8579 Japan Faculty of Science and Technology Tohoku Bunka Gakuen University Sendai-shi 981-8551 Japan

In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.

关键词： Speech recognition

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Learning GMM Using Elliptically Contoured Distributions

Learning GMM Using Elliptically Contoured Distributions

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International Conference on pattern recognition

作者： Bo Li Wenju Liu Lihua Dou Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China Beijing Institute of Technology Beijing Beijing CN

Model order selection and parameter estimation for Gaussian mixture model (GMM) are important issues for clustering analysis and density estimation. Most methods for model selection usually add a penalty term in the objective function that can penalize the models and choose an optimal one from a set of candidate models. This paper presents a simple and novel approach to determine the number of components and simultaneously estimate the parameters for GMM. By introducing the degenerating model, the proposed approach overcomes the drawback of likelihood estimate that is a non-decreasing function and can not be used to select the number of components. The degenerating model is a more general form of mixture component density and it can degenerate into the component density or a crater-like density when its parameter K varies from 1 to a bigger value. The likelihood of the crater-like density evaluated for the training data approximates to zero. This characteristic of the degenerating model forms the foundation of the proposed approach. The experimental results show robust and evident performance improvement of the approach.

关键词： Hidden Markov models Training Programming Gaussian distribution Density functional theory Computational modeling Estimation

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Improved discriminative training for generative model

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The Journal of China Universities of Posts and Telecommunications 2009年第3期16卷 126-130页

作者： WU Ya-hui,GUO Jun,LIU Gang laboratory of pattern recognition and intelligent system,Beijing University of Posts and Telecommunications,Beijing 100876,China [a]Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommunications Beijing 100876 China

This article proposes a model combination method to enhance the discriminability of the generative model. Generative and discriminative models have different optimization objectives and have their own advantages and drawbacks. The method proposed in this article intends to strike a balance between the two models mentioned above. It extracts the discriminative parameter from the generative model and generates a new model based on a multi-model combination. The weight for combining is determined by the ratio of the inter-variance to the intra-variance of the classes. The higher the ratio is, the greater the weight is, and the more discriminative the model will be. Experiments on speech recognition demonstrate that the performance of the new model outperforms the model trained with the traditional generative method.

关键词： model combination discriminability generative model training speech recognition

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HCL2000 - A large-scale handwritten Chinese character database for handwritten character recognition

HCL2000 - A large-scale handwritten Chinese character databa...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Zhang, Honggang Guo, Jun Chen, Guang Li, Chunguang Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent System Laboratory China

ISBN: (纸本)9780769537252

In this paper, we present a large scale off-line handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinese characters written by 1,000 different subjects. The writers' information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose. © 2009 IEEE.

关键词： Database systems

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Improved algorithms of music information retrieval based on audio fingerprint

Improved algorithms of music information retrieval based on ...

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3rd International Symposium on intelligent Information Technology Application Workshops, IITAW 2009

作者： Tang, Jie Liu, Gang Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications China

ISBN: (纸本)9780769538600

There are two important research topics in the field of Music Information Retrieval (MIR). One is how to improve the robustness of features and the other is how to speed up the retrieval process. This paper improved the algorithms which proposed by Shazam company in these two aspects. We improve the robustness of the system by a new audio finger-printing extraction using computer graphics, and the system can recognize the recordings which get in complex environment accurately. On the other hand, we propose a recursive search algorithm based on the confidence measure to improve the retrieval speed. Quantitative analysis of the opposite experiment verifies the improvement in the retrieval speed and accuracy. © 2009 IEEE.

关键词： Computer vision

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A visualization algorithm for alarm association mining

A visualization algorithm for alarm association mining

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2009 IEEE International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC2009

作者： Xu, Qianfang Li, Chunguang Xiao, Bo Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781424448982

Currently those algorithms to mine the alarm association rules are limited to the minimal support, so that they can only obtain the association rules among the frequently occurring alarm events, furthermore, the rules couldn't be visual display. This paper provides a novel mining alarm correlation visualization algorithm based on the non-linear reduced-feature mapping. The algorithm firstly projects the alarms on multidimensional space according to co-occurrence strength of the alarms, and then reduces the dimensions of the space, finally provides the relationship of the alarms to user with visualization. Experimental results based on synthetic and real datasets demonstrated that this algorithm not only discovered correlation among alarms, but also acquired the fault in the telecommunications network based on the graph transformation. ©2009 IEEE.

关键词： Data visualization

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N-LBP BASED VEHICLE MONITORING system

N-LBP BASED VEHICLE MONITORING SYSTEM

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Kunyan Lan Jun Guo Wenting Lu Honggang Zhang Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommun Laboratory of Pattern Recognition and Intelligent System Beijing University of Posts and Telecommuni

ISBN: (纸本)9781424448999

In recent years, feature based object detection has attracted increasing attention in computer vision research community. However, to our best knowledge, no previous work has focused on utilizing local binary pattern (LBP) for vehicle detection in intelligent Transportation system（ITS） domain. In this paper, we develop a novel traffic monitoring system based on N-LBP algorithm, which is the new LBP texture descriptor proposed. The approach includes three steps: firstly the general critical ingredients (GCI for short) are selected from LBP features through training to indicate vehicles. Then GCI are extracted from region of interest (ROI) in the new image for object detection and identification. Linear Kalman filter is employed for feature based tracking finally. Experimental results demonstrate the superiority of N-LBP feature over basic LBP feature, and performance of the new system is more stable and reliable.

关键词： ITS vehicle detection vehicle tracking N-LBP Kalman filter

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