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检索条件"主题词=pattern clustering methods"

共 22 条记录，以下是11-20 订阅

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An information-based clustering approach for fMRI activation detection

An information-based clustering approach for fMRI activation...

引用

5th IEEE International Symposium on Biomedical Imaging

作者： Bai, Lijun Qin, Wei Liang, Jimin Tian, Jie Xidian Univ Life Sci Res Ctr Xian Peoples R China Chinese Acad Sci Inst Automat Med Image Proc Grp Beijing 100864 Peoples R China

ISBN: (纸本)9781424420025

Most clustering algorithms in fMRI analysis implicitly require some nontrivial assumption on data structure. Due to arbitrary distribution of fMRI time series in the temporal domain, such analysis may mislead and limit the detector's performance. In this work, the authors exploited the application of an information-based clustering algorithm (Iclust) which could avoid these assumptions and provide many other benefits, such as no cluster shape restriction, no need of a prior definition about similarity measure, and the ability of capturing both linear and nonlinear dependence. Results from both artificial and real fMRI data indicated that the proposed framework could achieve better spatiotemporal accuracy, and enabled the exploration of fine furictional distinction of the human visual system in accordance with its well-known anatomy organizations.

关键词： pattern clustering methods magnetic resonance imaging

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LEARNING EFFICIENT CODES FOR 3D FACE RECOGNITION

LEARNING EFFICIENT CODES FOR 3D FACE RECOGNITION

引用

15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Zhong, Cheng Sun, Zhenan Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781424417650

Face representation based on the Visual Codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficient codes to represent the facial characteristics. In this paper, we introduce the Quadtree clustering algorithm to learn the facial-codes to boost 3D face recognition performance. The merits of Quadtree clustering come from: (1) It is robust to data noises;(2) It can adaptively assign clustering centers according to the density of data distribution. We make a comparison between Quadtree and some widely used clustering methods, such as G-means, K-means, Normalized-cut and Mean-shift. Experimental results show that using the facial-codes learned by Quadtree clustering gives the best performance for 3D face recognition.

关键词： Face recognition pattern clustering methods Image texture analysis pattern recognition Image analysis

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CUSTOM ON-DEMAND AND DATA ACCESS SERVICES FOR NEWS MERGED A-TRAIN DATA 30

CUSTOM ON-DEMAND AND DATA ACCESS SERVICES FOR NEWS MERGED A-...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Hua, Hook Fetzer, Eric Lewis, Steven Henderson, Mathew Guillaume, Alexandre Lee, Seungwon Juarez, Manuel de la Torre Dang, Van Braverman, Amy CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9781424495665

To simplify access to large and complex satellite data sets for climate analysis and model verification, we developed a web service tool that is used to study long-term and global-scale trends in climate, water and energy cycle, and weather variability. A related NASA Energy and Water Cycle Study (NEWS) task has created a merged NEWS Level 2 data from multiple instruments in NASA's A-Train constellation of satellites. We used this data to enable creation of climatologies that include correlation between observed temperature, water vapor and cloud properties from the A-Train sensors. Instead of imposing on the user an often rigid and limiting web-based analysis environment, we recognize the need for simple and well-designed distributed services so that users can perform analysis in their own familiar computing environments. A Federated OpenSearch capability with full text + space + time search of data products also facilitated interoperability with other data systems.

关键词： Information systems Meteorology pattern clustering methods Data processing Data management

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Soft geodesic kernel K-MEANS

Soft geodesic kernel <i>K</i>-MEANS

引用

32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kim, Joehwan Shim, Kwang-Hyun Choi, Seungiin ETRI Digital Content Res Div Taejon South Korea POSTECH Dept Comp Sci Pohang South Korea

ISBN: (纸本)1424407281

In this paper we present a kernel method for data clustering, where the soft k-means is carried out in a feature space, instead of input data space, leading to soft kernel k-means. We also incorporate a geodesic kernel into the soft kernel k-means, in order to take the data manifold structure into account. The method is referred to as soft geodesic kernel k-means. In contrast to k-means, our method is able to identify clusters that are not linearly separable. In addition, soft responsibilities as well as geodesic kernel, improve the clustering performance, compared to kernel k-means. Numerical experiments with toy data sets and real-world data sets (UCI and document clustering), confirm the useful behavior of the proposed method.

关键词： pattern clustering methods pattern classification unsupervised learning

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Video cataloging based on robust logotype detection

Video cataloging based on robust logotype detection

引用

IEEE International Conference on Image Processing (ICIP 2006)

作者： Cozar, J. R. Guil, N. Gonzalez-Linares, J. M. Zapata, E. L. Univ Malaga Dept Comp Architecture E-29071 Malaga Spain

ISBN: (纸本)9781424404810

In this paper a technique for video cataloging based on logo detection is shown. No a priori knowledge about shape or spatial-temporal location of logos is assumed. The method implements a new algorithm for on-line logo detection based on temporal and spatial segmentation of broadcasted videos. Temporal segmentation identifies constant luminance regions within video frames while spatial segmentation helps to refine previous segmented regions. In a final step, identified logos are searched in a database and classified into candidate or learnt logotypes. Learnt logos can be directly tracked through the video. Candidate logotypes are assigned to a cluster of similar logos. After a promotion process, all the candidate logos belonging to the same cluster are used to create a new learnt logotype.

关键词： video signal processing multimedia databases image segmentation pattern clustering methods

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Cooperative swarms for clustering phoneme data

Cooperative swarms for clustering phoneme data

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14th IEEE/SP Workshop on Statistical Signal Processing

作者： Ahmadi, Abbas Karray, Fakhri Kamel, Mohamed Univ Waterloo Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424411979

A new approach for clustering phoneme data is proposed. The proposed approach mimics the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and, in a same time, in regions far from other places. We tackle phoneme clustering problem using particle swarm optimization(PSO) and utilizing multiple cooperating swarms to obtain cluster centers. The proposed approach is evaluated on phoneme data of TIMIT Experimental results show that the proposed clustering approach outperforms single swarm-based clustering as well as k-means, k-harmonic means, and fuzzy c-means clustering approaches.

关键词： cooperative systems pattern clustering methods optimization methods speech recognition

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A method of initialization for nonnegative matrix factorization

A method of initialization for nonnegative matrix factorizat...

引用

32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kim, Yong-Deok Choi, Selingiin POSTECH Dept Comp Sci Seoul South Korea

ISBN: (纸本)1424407281

Nonnegative matrix factorization (NMF) is a widely-used method for multivariate nonnegative data analysis, due to its ability to learn a parts-based representation. However, the standard NMF algorithm does not always find spatially localized basis images in practice, unless sparseness constraints are employed. In this paper we present a method of structured initialization which enables the standard NMF algorithm to find spatially localized basis images. The initialization method is based on the hierarchical clustering of attributes through a similarity measure reflecting 'closeness to rank-one'. Numerical experiments with face image data, confirm the validity of our initialization method.

关键词： feature extraction matrix decomposition pattern clustering methods pattern classification unsupervised learning

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A robust approach towards sequential data modeling and its application in automatic gesture recognition

A robust approach towards sequential data modeling and its a...

引用

33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chatzis, Sotirios Kosmopoulos, Dimitrios I. Varvarigou, Theodora Natl Tech Univ Athens 9 Iroon Polytech Str GR-15773 Athens Greece NCSR Demokritos GR-15310 Athens Greece

ISBN: (纸本)9781424414833

Hidden Markov models using finite Gaussian mixture models as their hidden state distributions have been applied in modeling of time series that result from various noisy signals. Nevertheless, Gaussian mixture models are well-known to be highly intolerant to the presence of outliers within the fitting sets used for their estimation. Finite Student's-t mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit those merits of Student's-t mixture models, we introduce in this paper a novel hidden Markov chain model where the hidden state distributions are considered to be finite mixtures of multivariate Student's-t densities and we derive an algorithm for the model parameters estimation under a maximum likelihood framework. We apply this novel approach in automatic gesture recognition and we show that our model provides a substantial improvement in data representation performance and computational efficiency over the standard Gaussian model.

关键词： pattern classification pattern clustering methods pattern recognition

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Kernel Based Hybrid Fuzzy clustering for Non-Linear Fuzzy Classifiers

Kernel Based Hybrid Fuzzy Clustering for Non-Linear Fuzzy Cl...

引用

Annual Meeting of the North-American-Fuzzy-Information-Processing-Society

作者： Celikyilmaz, Asli Turksen, I. Burhan Univ Calif Berkeley Dept Comp Sci Berkeley CA 94720 USA TOBB Econ & Technol Univ Ind Engn Dept Ankara Turkey Univ Toronto Med & Ind Engn Dept Toronto ON Canada

ISBN: (纸本)9781424445752

In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives are balanced with a regularization term. An additional merit of the novel method is that the information on natural groupings of the data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function learnt from the non-linear SVC to improve the accuracy of the classifier model. The comparative experiments demonstrate the effectiveness of the proposed method in building a classifier model for a detection system.

关键词： hybrid fuzzy clustering kernels pattern clustering methods

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On the use of metrics for multi-dimensional descriptors clustering

On the use of metrics for multi-dimensional descriptors clus...

引用

IEEE International Conference on Image Processing (ICIP 2006)

作者： Houissa, Hichem Boujemaa, Nozha INRIA Rocquencourt Domaine de Voluceau BP 105 F-78153 Le Chesnay France

ISBN: (纸本)9781424404810

The Visual Thesaurus is a new query approach when no starting image is available. It is a concise representation of all similar regions in a panel of visual patches;the user arranges the visual patches according to his mental target image. The construction of the Visual Thesaurus needs a reliable region description and a clustering algorithm that reflects the variety of the database. In this paper, we develop a new region description schema based on Harris color points of interest. We also evaluate the relevance of several multi-dimensional matching metrics when measuring the similarity between regions described by variable signature dimensions. We outline the need of clustering to speed up the computation process as well. Moreover, we adopted the relational clustering algorithm to categorize regions according to Harris points of interest features. Generated clusters are represented by prototypes that compose the "page zero" of the Visual Thesaurus. We tested our approach on generic database, the relevance of obtained clusters is evaluated subjectively.

关键词： image region analysis pattern matching image texture analysis image classification pattern clustering methods image matching

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