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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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Fuzzy classification using pattern discovery

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2007年第5期15卷 772-783页

作者： Hamilton-Wright, Andrew Stashuk, Daniel W. Tizhoosh, Hamid R. Univ Guelph Dept Comp & Informat Sci Guelph ON N1G 2W1 Canada Mt Allison Univ Dept Math & Comp Sci Sackville NB E4L 1E6 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

Rule-based classifiers allow rationalization of classifications made. This in turn improves understanding which is essential for effective decision support. As a rule based classifier, the pattern Discovery (PD) algorithm functions well in discrete, nominal and continuous data domains. A drawback when using PD as a classifier for decision support is that it has an unbounded decision space that confounds the understanding of the degree of support for a decision. Incorporating PD, into a fuzzy inference system (FIS) allows the the degree of support for a decision to be expressed with intuitively understandable terms. In addition, using discrete algorithms in continuous domains can result in reduced accuracy due to quantization. Fuzzification reduces this "cost of quantization" and improves classification performance. In this work, the PD algorithm was used as a source of rules for a series of FISs implemented using different rule weighting and defuzzification schemes, each providing a linguistic basis for rule description and a [-1... 1] bounded space for expression of decision support. The output of each FIS consists of a suggested outcome, a strong confidence metric describing suggestions within this space and a linguistic expression of the rules. This constitutes a stronger basis for decision making than that provided by PD alone. A variety of synthetic, continuous class distributions with varying degrees of separation was used to evaluate the performance of fuzzy, PD, back-propagation and Bayesian classifiers. Overall, the accuracy of the fuzzy system was found to be similar, but slightly below, that of the inherently continuous valued classifiers and was somewhat improved with respect to the PD classifiers. For the difficult spiral class distributions studied, the fuzzy classifiers were able to make more classifications than the PD classifiers. The correct classification rates for the fuzzy classifiers were similar across the various rule weighting and defuzzific

关键词： decision support systems deductive databases fuzzy logic fuzzy systems pattern classification pattern clustering methods pattern matching statistical databases

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Statistical models for assessing the individuality of fingerprints

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2007年第3期2卷 391-401页

作者： Zhu, Yongfang Dass, Sarat C. Jain, Anil K. Michigan State Univ Dept Stat & Probabil E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Following the Daubert ruling in 1993, forensic evidence based on fingerprints was first challenged in the 1999 case of the U.S. versus Byron C. Mitchell and, subsequently, in 20 other cases involving fingerprint evidence. The main concern with the admissibility of fingerprint evidence is the problem of individualization, namely, that the fundamental premise for asserting the uniqueness of fingerprints has not been objectively tested and matching error rates are unknown. In order to assess the error rates, we require quantifying the variability of fingerprint features, namely, minutiae in the target population. A family of finite mixture models has been developed in this paper to represent the distribution of minutiae in fingerprint images, including minutiae clustering tendencies and dependencies in different regions of the fingerprint image domain. A mathematical model that computes the probability of a random correspondence (PRC) is derived based on the mixture models. A PRC of 2.25 x 10(-6) corresponding to 12 minutiae matches was computed for the NIST4 Special Database, when the numbers of query and template minutiae both equal 46. This is also the estimate of the PRC for a target population with a similar composition as that of. NIST4.

关键词： fingerprint identification image processing pattern classification pattern clustering methods pattern matching statistics

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Soft geodesic kernel K-MEANS

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

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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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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...

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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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P2P traffic identification using cluster analysis

P2P traffic identification using cluster analysis

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1st International Global Information Infrastructure Symposium

作者： Siqueira Junior, Gabriel Paulino Bessa Maia, Jose Everardo Holanda, Raimir de Sousa, Jose Neuman Univ Fortaleza Appl Comp Sci Dept Fortaleza Ceara Brazil Univ Estadual Ceara Dept Comp Sci & Stat Fortaleza Ceara Brazil Univ Fortaleza Appl Comp Sci Dept Fortaleza Ceara Brazil Univ Fed Ceara Dept Comp Sci Member IEEE Fortaleza Ceara Brazil

ISBN: (纸本)9781424413751

In the last years, we have seen that the use of P2P applications has increased significantly and currently they represent a significant portion of the Internet traffic. In consequence of this growth, P2P traffic characterization and identification are becoming increasingly important for network administrators and designers. However, this idendfication is not simple. Nowadays, P2P applications explicitly try to camouflage the original traffic in an attempt to go undetected This work presents a methodology and selection of five traffic discriminators and applies cluster analysis to identify P2P applications. Our results indicate that the accuracy is around 90% using this small number of discriminators.

关键词： pattern classification statistics pattern clustering methods Internet

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Video cataloging based on robust logotype detection

Video cataloging based on robust logotype detection

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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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On the use of metrics for multi-dimensional descriptors clustering

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

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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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A cluster-based approach for detecting man-made objects and changes in imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2005年第2期43卷 374-387页

作者： Carlotto, MJ Gen Dynam Adv Informat Syst Arlington VA 22209 USA

A new unified approach to object and change detection is presented that involves clustering and analyzing the distribution of pixel values within clusters over one or more images. Cluster-based anomaly detection (CBAD) can detect man-made objects that are: 1) present in a single multiband image;2) appear or disappear between two images acquired at different times;or 3) manifest themselves as spectral differences between two sets of bands acquired at the same time. Based on a Gaussian mixture model, CBAD offers an alternative to compute-intensive, sliding-window algorithms like Reed and Yu's RX-algorithm for single-image object detection. It assumes that background pixel values within clusters can be modeled as Gaussian distributions about mean values that vary cluster-to-cluster and that anomalies (man-made objects) have values that deviate significantly from the distribution of the cluster. This model is valid in situations where the frequency of occurrence of man-made objects is low compared to the background so that they do not form distinct clusters, but are instead split up among multiple background clusters. CBAD estimates background statistics over clusters, not sliding windows, and so can detect objects of any size or shape. This provides the flexibility of filtering detections at the object level. Examples show the ability to detect small compact objects such as vehicles as well as large, spatially extended features (e.g., built-up and bomb-damaged areas). Unlike previous approaches to change detection, which compare pixels, vectors, features, or objects, cluster-based change detection involves no direct comparison of images. In fact, it is identical to the object detection algorithm, different only in the way it is applied. Preliminary results show cluster-based change detection is less sensitive to image misregistration errors than global change detection. The same cluster-based algorithm can also be used for cross-spectral anomaly detection. An example s

关键词： change detection Gaussian processes image analysis image representations image sequence analysis object detection pattern clustering methods vector quantization

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