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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

共 7554 条记录，以下是4981-4990 订阅

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Mutual information based on Renyi's entropy feature selection

Mutual information based on Renyi's entropy feature selectio...

引用

IEEE international conference on Intelligent computing and Intelligent Systems (ICIS)

作者： Can-Tao Liu Bao-Gang Hu National Laboratory of Pattern Recognition /Sino-French Laboratory in Computer Science Automation and Applied Mathematics Institute of Automation Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

Feature selection problem has become the focus of much pattern classification research and mutual information is more and more important in the feature selection algorithms. We proposed normalized mutual information based on Renyi's quadratic entropy feature selection, which reduces the computational complexity, relying on the efficient estimation of the mutual information. Then we combine NMIFS with wrappers into a two-stage feature selection algorithm. This helps us find more charactering feature subset. We perform some experiments to compare the efficiency and classification accuracy to other MI-based feature selection algorithm. Results show that our method leads to promising improvement on computation complexity.

关键词： Mutual information Entropy Computational complexity Filters Laboratories pattern recognition Automation Machine learning algorithms Degradation Computer science

来源：评论

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Video scene segmentation using time constraint dominant-set clustering

Video scene segmentation using time constraint dominant-set ...

引用

16th international Multimedia Modeling conference on Advances in Multimedia Modeling, MMM 2010

作者： Zeng, Xianglin Zhang, Xiaoqin Hu, Weiming Li, Wanqing National Laboratory of Pattern Recognition Institute of Automation Beijing China SCSSE University of Wollongong Australia

ISBN: (纸本)3642113001

Video scene segmentation plays an important role in video structure analysis. In this paper, we propose a time constraint dominant-set clustering algorithm for shot grouping and scene segmentation, in which the similarity between shots is based on autocorrelogram feature, motion feature and time constraint. Therefore, the visual evidence and time constraint contained in the video content are effectively incorporated into a unified clustering framework. Moreover, the number of clusters in our algorithm does not need to be predefined and thus it provides an automatic framework for scene segmentation. Compared with normalized cut clustering based scene segmentation, our algorithm can achieve more accurate results and requires less computing resources. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Clustering algorithms

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Context-independent phoneme recognition using a K-Nearest Neighbour classification approach

Context-independent phoneme recognition using a K-Nearest Ne...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ladan Golipour Douglas O'Shaughnessy INRS-EMT Quebec University Montreal Canada

In this paper we investigate a non-parametric classification of English phonemes in speaker-independent continuous speech. We employ the ldquovotingrdquo k-nearest neighbour (k-NN) classifier, a powerful technique in pattern recognition problems, along with a new representation of phonemes for the speech recognition task. We also exploit the idea behind ldquoapproximaterdquo k-NN that results in a very fast way of computing the k approximate closest neighbours of each data point. Comparing the recognition performance of the proposed method with the HMM-based recognizer of HTK toolkit reveals that the k-NN-based recognizer outperforms its counterpart. In addition, incorporating the ldquoapproximaterdquo nearest neighbour search instead of the ldquoexactrdquo one results in completing the training step much faster than the HMM-based system, and the testing step with a comparable computational time. We also reduced the amount of the training data by applying a pattern recognition technique, called ldquothinningrdquo algorithm. The outcome was a considerable reduction in the k-NN search space and hence the execution time, and also a slight increase in the recognition performance.

关键词： Speech recognition Hidden Markov models Error analysis Voting pattern recognition Training data pattern classification Loudspeakers Humans Automatic speech recognition

来源：评论

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Extended Bipolar Echo Kernel for Audio Watermarking

Extended Bipolar Echo Kernel for Audio Watermarking

引用

international conference on Advances in Recent Technologies in Communication and computing, ARTCom

作者： Krishna Kumar S. K.P. Shine Centre for Dev. of Adv. Comput. Broadcast & Commun. Group Trivandrum India

In echo hiding, watermark data is embedded into the host signal by introducing echoes of different delays and is subsequently extracted using cepstral analysis. The choice of echo kernel determines the fidelity and robustness performance of the watermarking scheme. In this paper we propose the extended bipolar echo kernel, which greatly improves the fidelity of the watermarked signal without compromising the detection performance.

关键词： Kernel Watermarking Image segmentation Clustering algorithms Color Computer vision Image recognition pattern recognition Machine learning Artificial intelligence

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Improved Chaotic Particle Swarm Optimization using circle map for training SVM

Improved Chaotic Particle Swarm Optimization using circle ma...

引用

international conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Ming Li Xiangfeng Sun Weijuan Li Yan Wang School of Computer and Communication Lanzhou University of Technology LanZhou China

Training SVM consumes large memory and computation time, but traditional methods can not overcome above shortcomings. This paper proposes an improved SVM training method based on chaotic particle swarm optimization (CPSO) using circle map. Firstly, a new chaotic search model using improved circle map is introduced. Then this new model is introduced into particle swarm optimization (PSO). Finally, the detail training SVM algorithm using this improved CPSO algorithm is presented. The experimental results on face database show that the proposed SVM method optimized by the improved CPSO can achieve higher recognition performance.

关键词： Chaos Particle swarm optimization Support vector machines Chaotic communication Support vector machine classification Logistics Quantum computing Face recognition Machine learning algorithms pattern recognition

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Using SIFT for the Filtering of Chinese Text in Image of Multimedia Message Service

Using SIFT for the Filtering of Chinese Text in Image of Mul...

引用

international conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： ChunXiao Ma Chuang Zhang ZhiQing Lin Pattern Recognition & Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

With the popularity of MMS, the multimedia messages which include sensitive information are increasing rapidly. In the paper, a novel framework of a MMS filtering for Chinese sensitive text in image is presented. An effective method is applied to detect and filter sensitive texts in image of multimedia message which could easily be transmitted through the mobile communication network without being monitored at recent stage. The detection and recognition of sensitive text are achieved by using SIFT feature, which is proper to the characteristics of the text in image of multimedia message and get an accurate result. The method has a good practical application value.

关键词： Message service Information filtering Information filters Multimedia systems Monitoring Image recognition Text recognition Internet Feature extraction Image resolution

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Singing voice recognition based on matching of spectrogram pattern

Singing voice recognition based on matching of spectrogram p...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Peerapol Khunarsal Chidchanok Lursinsap Thanapant Raicharoen Advanced Virtual and Intelligent Computing Center (AVIC) Department of Mathematics Chulalongkom University Bangkok Thailand Department The Office of Permanent Secretary of Defence Ministry of Defence Defence Information and Space Technology Bangkok Thailand

Singing voice recognition is a difficult topic in music information retrieval research area. The first approaches borrowed successful techniques widely used in automatic speech recognition (ASR) as speech and singing share similar acoustical feature since they are produced by the same apparatus. Moving from monophonic to polyphonic audio signal the problem become more complex as the background instrumental accompaniment is regarded as a noise source that has to be attenuated. This paper proposes a singing voice recognition algorithm that is able to automatically recognize the word in a singing signal with background music by using the concept of spectrogram pattern matching. The main idea is to apply both the spectrogram and the image processing methods to solve the problem of singing voice recognition. Each signal that accompanies music is analyzed and generated to its spectrogram that is used to train data for the classifier. Several classification functions are compared, such as Fisher classifier, feed-forward can effectively recognize the word in music with the accuracy rate more than 84%.

关键词： pattern recognition Speech recognition pattern matching Spectrogram Automatic speech recognition Multiple signal classification Music information retrieval Instruments Background noise Image processing

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Interval regression analysis with soft-margin reduced support vector machine

Interval regression analysis with soft-margin reduced suppor...

引用

22nd international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009

作者： Huang, Chia-Hui Kao, Han-Ying Department of Information Management Kainan University No.1 Kainan Road Luzhu Shiang Taoyuan 33857 Taiwan Department of Computer and Information Science National Dong Hwa University No.123 Hua-Shi Road Hualien 97063 Taiwan

ISBN: (纸本)3642025676

The support vector machine (SVM) has shown to be an efficient approach for a variety of classification problems. It has also been widely used in pattern recognition, regression and distribution estimation for crisp data. However, there are three main problems while using SVM model: (1) Large-scale: when dealing with large-scale data sets, the solution by using SVM with nonlinear kernels may be difficult to find;(2) Unbalance: the number of samples from one class is much larger than the number of samples from other classes. It causes the excursion of separation margin;(3) Noises and Interaction: the distribution of data becomes hard to be described and the separation margin between classes becomes a "gray" zone. Under this circumstance, to develop an efficient method is necessary. Recently the reduced support vector machine (RSVM) was proposed as an alternative of the standard SVM. It has been proved more efficient than the traditional SVM in processing large-scaled data. In this paper we introduce the principle of RSVM to evaluate interval regression analysis. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone. © 2009 Springer Berlin Heidelberg.

关键词： Support vector machines

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pattern recognition system based on decision trees and fuzzy logic: Anti-HIV molecules application

Pattern recognition system based on decision trees and fuzzy...

引用

IEEE international conference on Multimedia computing and Systems (ICMCS)

作者： Mohamed Kissi Mohammed Ramdani Driss Cherqaoui Department de Mathematiques et Informatique Faculté des Sciences El Jadida Morocco Department d'informatique (LIM@II) Faculté des Sciences et Techniques Mohammedia Morocco Department de chimie Faculté des Sciences Semlalia Marrakech Morocco

Several works structure activity relationship (SAR) of anti-HIV molecules (Human Immunodeficiency Virus) were studied by different statistical methods and non-linear models (neural networks). But few studies have used the heuristic methods. In this work, we are interested to study this relationship by fuzzy logic and decision trees. The resulting model explain SAR with only tow rules described by three of 7 molecular descriptors. This rules generalize the 79 compounds studied. Decision trees show good performance in the learning and prediction phases.

关键词： pattern recognition Decision trees Fuzzy logic Human immunodeficiency virus Statistical analysis Artificial neural networks Testing Neural networks Chemical compounds Linear regression

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A Statistics and Local Homogeneity Based Color Edge Detection Algorithm

A Statistics and Local Homogeneity Based Color Edge Detectio...

引用

international conference on Advances in Recent Technologies in Communication and computing, ARTCom

作者： Soumya Dutta Bidyut B. Chaudhuri Department of Electronics and Communication Netaji Subhash Engineering College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A novel color edge detection algorithm is proposed in this paper. On the basis of standard deviation calculation of pixels the discontinuity among the pixels are detected. Then the image is segmented into a binary image with a fixed threshold where black pixels signify homogeneous region and white pixels signify edges. Finally, a thinning technique is applied to extract thin edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Statistics Image edge detection Image processing Object recognition Shape measurement Image segmentation Pixel pattern recognition Area measurement Computer vision

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